Source code for MDAnalysis.analysis.base

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# Please cite your use of MDAnalysis in published work:
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# 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.
# MDAnalysis: A Python package for the rapid analysis of molecular dynamics
# simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th
# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.
# doi: 10.25080/majora-629e541a-00e
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# N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein.
# MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.
# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
#
"""Analysis building blocks --- :mod:`MDAnalysis.analysis.base`
============================================================

MDAnalysis provides building blocks for creating analysis classes. One can
think of each analysis class as a "tool" that performs a specific analysis over
the trajectory frames and stores the results in the tool.

Analysis classes are derived from :class:`AnalysisBase` by subclassing. This
inheritance provides a common workflow and API for users and makes many
additional features automatically available (such as frame selections and a
verbose progressbar). The important points for analysis classes are:

#. Analysis tools are Python classes derived from :class:`AnalysisBase`.
#. When instantiating an analysis, the :class:`Universe` or :class:`AtomGroup`
   that the analysis operates on is provided together with any other parameters
   that are kept fixed for the specific analysis.
#. The analysis is performed with :meth:`~AnalysisBase.run` method. It has a
   common set of arguments such as being able to select the frames the analysis
   is performed on. The `verbose` keyword argument enables additional output. A
   progressbar is shown by default that also shows an estimate for the
   remaining time until the end of the analysis.
#. Results are always stored in the attribute :attr:`AnalysisBase.results`,
   which is an instance of :class:`Results`, a kind of dictionary that allows
   allows item access via attributes. Each analysis class decides what and how
   to store in :class:`Results` and needs to document it. For time series, the
   :attr:`AnalysisBase.times` contains the time stamps of the analyzed frames.


Example of using a standard analysis tool
-----------------------------------------

For example, the :class:`MDAnalysis.analysis.rms.RMSD` performs a
root-mean-square distance analysis in the following way:

.. code-block:: python

   import MDAnalysis as mda
   from MDAnalysisTests.datafiles import TPR, XTC

   from MDAnalysis.analysis import rms

   u = mda.Universe(TPR, XTC)

   # (2) instantiate analysis
   rmsd = rms.RMSD(u, select='name CA')

   # (3) the run() method can select frames in different ways
   # run on all frames (with progressbar)
   rmsd.run(verbose=True)

   # or start, stop, and step can be used
   rmsd.run(start=2, stop=8, step=2)

   # a list of frames to run the analysis on can be passed
   rmsd.run(frames=[0,2,3,6,9])

   # a list of booleans the same length of the trajectory can be used
   rmsd.run(frames=[True, False, True, True, False, False, True, False,
                    False, True])

   # (4) analyze the results, e.g., plot
   t = rmsd.times
   y = rmsd.results.rmsd[:, 2]   # RMSD at column index 2, see docs

   import matplotlib.pyplot as plt
   plt.plot(t, y)
   plt.xlabel("time (ps)")
   plt.ylabel("RMSD (Å)")


Writing new analysis tools
--------------------------

In order to write new analysis tools, derive a class from :class:`AnalysisBase`
and define at least the :meth:`_single_frame` method, as described in
:class:`AnalysisBase`.

.. SeeAlso::

   The chapter `Writing your own trajectory analysis`_ in the *User Guide*
   contains a step-by-step example for writing analysis tools with
   :class:`AnalysisBase`.

.. _`Writing your own trajectory analysis`:
   https://userguide.mdanalysis.org/stable/examples/analysis/custom_trajectory_analysis.html


If your analysis is operating independently on each frame, you might consider
making it **parallelizable** via adding a :meth:`get_supported_backends` method,
and appropriate aggregation function for each of its results. For example, if
you have your :meth:`_single_frame` method storing important values under
:attr:`self.results.timeseries`, you will write:

.. code-block:: python

    class MyAnalysis(AnalysisBase):
        _analysis_algorithm_is_parallelizable = True

        @classmethod
        def get_supported_backends(cls):
            return ('serial', 'multiprocessing', 'dask',)

        
        def _get_aggregator(self):
          return ResultsGroup(lookup={'timeseries': ResultsGroup.ndarray_vstack})

See :mod:`MDAnalysis.analysis.results` for more on aggregating results.

.. SeeAlso::

   :ref:`parallel-analysis`



Classes
-------

The :class:`MDAnalysis.results.Results` and :class:`AnalysisBase` classes
are the essential building blocks for almost all MDAnalysis tools in the
:mod:`MDAnalysis.analysis` module. They aim to be easily useable and
extendable.

:class:`AnalysisFromFunction` and the :func:`analysis_class` functions are
simple wrappers that make it even easier to create fully-featured analysis
tools if only the single-frame analysis function needs to be written.

"""
import inspect
import itertools
import logging
import warnings
from functools import partial
from typing import Iterable, Union

import numpy as np
from .. import coordinates
from ..core.groups import AtomGroup
from ..lib.log import ProgressBar

from .backends import BackendDask, BackendMultiprocessing, BackendSerial, BackendBase
from .results import Results, ResultsGroup

logger = logging.getLogger(__name__)


[docs] class AnalysisBase(object): r"""Base class for defining multi-frame analysis The class is designed as a template for creating multi-frame analyses. This class will automatically take care of setting up the trajectory reader for iterating, and it offers to show a progress meter. Computed results are stored inside the :attr:`results` attribute. To define a new Analysis, :class:`AnalysisBase` needs to be subclassed and :meth:`_single_frame` must be defined. It is also possible to define :meth:`_prepare` and :meth:`_conclude` for pre- and post-processing. All results should be stored as attributes of the :class:`MDAnalysis.analysis.results.Results` container. Parameters ---------- trajectory : MDAnalysis.coordinates.base.ReaderBase A trajectory Reader verbose : bool, optional Turn on more logging and debugging Attributes ---------- times: numpy.ndarray array of Timestep times. Only exists after calling :meth:`AnalysisBase.run` frames: numpy.ndarray array of Timestep frame indices. Only exists after calling :meth:`AnalysisBase.run` results: :class:`Results` results of calculation are stored after call to :meth:`AnalysisBase.run` Example ------- .. code-block:: python from MDAnalysis.analysis.base import AnalysisBase class NewAnalysis(AnalysisBase): def __init__(self, atomgroup, parameter, **kwargs): super(NewAnalysis, self).__init__(atomgroup.universe.trajectory, **kwargs) self._parameter = parameter self._ag = atomgroup def _prepare(self): # OPTIONAL # Called before iteration on the trajectory has begun. # Data structures can be set up at this time self.results.example_result = [] def _single_frame(self): # REQUIRED # Called after the trajectory is moved onto each new frame. # store an example_result of `some_function` for a single frame self.results.example_result.append(some_function(self._ag, self._parameter)) def _conclude(self): # OPTIONAL # Called once iteration on the trajectory is finished. # Apply normalisation and averaging to results here. self.results.example_result = np.asarray(self.example_result) self.results.example_result /= np.sum(self.result) Afterwards the new analysis can be run like this .. code-block:: python import MDAnalysis as mda from MDAnalysisTests.datafiles import PSF, DCD u = mda.Universe(PSF, DCD) na = NewAnalysis(u.select_atoms('name CA'), 35) na.run(start=10, stop=20) print(na.results.example_result) # results can also be accessed by key print(na.results["example_result"]) .. versionchanged:: 1.0.0 Support for setting `start`, `stop`, and `step` has been removed. These should now be directly passed to :meth:`AnalysisBase.run`. .. versionchanged:: 2.0.0 Added :attr:`results` .. versionchanged:: 2.8.0 Added ability to run analysis in parallel using either a built-in backend (`multiprocessing` or `dask`) or a custom `backends.BackendBase` instance with an implemented `apply` method that is used to run the computations. """
[docs] @classmethod def get_supported_backends(cls): """Tuple with backends supported by the core library for a given class. User can pass either one of these values as ``backend=...`` to :meth:`run()` method, or a custom object that has ``apply`` method (see documentation for :meth:`run()`): - 'serial': no parallelization - 'multiprocessing': parallelization using `multiprocessing.Pool` - 'dask': parallelization using `dask.delayed.compute()`. Requires installation of `mdanalysis[dask]` If you want to add your own backend to an existing class, pass a :class:`backends.BackendBase` subclass (see its documentation to learn how to implement it properly), and specify ``unsupported_backend=True``. Returns ------- tuple names of built-in backends that can be used in :meth:`run(backend=...)` .. versionadded:: 2.8.0 """ return ("serial",)
# class authors: override _analysis_algorithm_is_parallelizable # in derived classes and only set to True if you have confirmed # that your algorithm works reliably when parallelized with # the split-apply-combine approach (see docs) _analysis_algorithm_is_parallelizable = False @property def parallelizable(self): """Boolean mark showing that a given class can be parallelizable with split-apply-combine procedure. Namely, if we can safely distribute :meth:`_single_frame` to multiple workers and then combine them with a proper :meth:`_conclude` call. If set to ``False``, no backends except for ``serial`` are supported. .. note:: If you want to check parallelizability of the whole class, without explicitly creating an instance of the class, see :attr:`_analysis_algorithm_is_parallelizable`. Note that you setting it to other value will break things if the algorithm behind the analysis is not trivially parallelizable. Returns ------- bool if a given ``AnalysisBase`` subclass instance is parallelizable with split-apply-combine, or not .. versionadded:: 2.8.0 """ return self._analysis_algorithm_is_parallelizable def __init__(self, trajectory, verbose=False, **kwargs): self._trajectory = trajectory self._verbose = verbose self.results = Results()
[docs] def _define_run_frames(self, trajectory, start=None, stop=None, step=None, frames=None ) -> Union[slice, np.ndarray]: """Defines limits for the whole run, as passed by self.run() arguments Parameters ---------- trajectory : mda.Reader a trajectory Reader start : int, optional start frame of analysis, by default None stop : int, optional stop frame of analysis, by default None step : int, optional number of frames to skip between each analysed frame, by default None frames : array_like, optional array of integers or booleans to slice trajectory; cannot be combined with ``start``, ``stop``, ``step``; by default None Returns ------- Union[slice, np.ndarray] Appropriate slicer for the trajectory that would give correct iteraction order via trajectory[slicer] Raises ------ ValueError if *both* `frames` and at least one of ``start``, ``stop``, or ``step`` is provided (i.e. set to not ``None`` value). .. versionadded:: 2.8.0 """ self._trajectory = trajectory if frames is not None: if not all(opt is None for opt in [start, stop, step]): raise ValueError("start/stop/step cannot be combined with frames") slicer = frames else: start, stop, step = trajectory.check_slice_indices(start, stop, step) slicer = slice(start, stop, step) self.start, self.stop, self.step = start, stop, step return slicer
[docs] def _prepare_sliced_trajectory(self, slicer: Union[slice, np.ndarray]): """Prepares sliced trajectory for use in subsequent parallel computations: namely, assigns self._sliced_trajectory and its appropriate attributes, self.n_frames, self.frames and self.times. Parameters ---------- slicer : Union[slice, np.ndarray] appropriate slicer for the trajectory .. versionadded:: 2.8.0 """ self._sliced_trajectory = self._trajectory[slicer] self.n_frames = len(self._sliced_trajectory) self.frames = np.zeros(self.n_frames, dtype=int) self.times = np.zeros(self.n_frames)
[docs] def _setup_frames(self, trajectory, start=None, stop=None, step=None, frames=None): """Pass a Reader object and define the desired iteration pattern through the trajectory Parameters ---------- trajectory : mda.Reader A trajectory Reader start : int, optional start frame of analysis stop : int, optional stop frame of analysis step : int, optional number of frames to skip between each analysed frame frames : array_like, optional array of integers or booleans to slice trajectory; cannot be combined with ``start``, ``stop``, ``step`` .. versionadded:: 2.2.0 Raises ------ ValueError if *both* `frames` and at least one of ``start``, ``stop``, or ``frames`` is provided (i.e., set to another value than ``None``) .. versionchanged:: 1.0.0 Added .frames and .times arrays as attributes .. versionchanged:: 2.2.0 Added ability to iterate through trajectory by passing a list of frame indices in the `frames` keyword argument .. versionchanged:: 2.8.0 Split function into two: :meth:`_define_run_frames` and :meth:`_prepare_sliced_trajectory`: first one defines the limits for the whole run and is executed once during :meth:`run` in :meth:`_setup_frames`, second one prepares sliced trajectory for each of the workers and gets executed twice: one time in :meth:`_setup_frames` for the whole trajectory, second time in :meth:`_compute` for each of the computation groups. """ slicer = self._define_run_frames(trajectory, start, stop, step, frames) self._prepare_sliced_trajectory(slicer)
[docs] def _single_frame(self): """Calculate data from a single frame of trajectory Don't worry about normalising, just deal with a single frame. Attributes accessible during your calculations: - ``self._frame_index``: index of the frame in results array - ``self._ts`` -- Timestep instance - ``self._sliced_trajectory`` -- trajectory that you're iterating over - ``self.results`` -- :class:`MDAnalysis.analysis.results.Results` instance holding run results initialized in :meth:`_prepare`. """ raise NotImplementedError("Only implemented in child classes")
[docs] def _prepare(self): """ Set things up before the analysis loop begins. Notes ----- ``self.results`` is initialized already in :meth:`self.__init__` with an empty instance of :class:`MDAnalysis.analysis.results.Results` object. You can still call your attributes as if they were usual ones, ``Results`` just keeps track of that to be able to run a proper aggregation after a parallel run, if necessary. """ pass # pylint: disable=unnecessary-pass
[docs] def _conclude(self): """Finalize the results you've gathered. Called at the end of the :meth:`run` method to finish everything up. Notes ----- Aggregation of results from individual workers happens in :meth:`self.run()`, so here you have to implement everything as if you had a non-parallel run. If you want to enable proper aggregation for parallel runs for you analysis class, implement ``self._get_aggregator`` and check :mod:`MDAnalysis.analysis.results` for how to use it. """ pass # pylint: disable=unnecessary-pass
[docs] def _compute(self, indexed_frames: np.ndarray, verbose: bool = None, *, progressbar_kwargs={}) -> "AnalysisBase": """Perform the calculation on a balanced slice of frames that have been setup prior to that using _setup_computation_groups() Parameters ---------- indexed_frames : np.ndarray np.ndarray of (n, 2) shape, where first column is frame iteration indices and second is frame numbers verbose : bool, optional Turn on verbosity progressbar_kwargs : dict, optional ProgressBar keywords with custom parameters regarding progress bar position, etc; see :class:`MDAnalysis.lib.log.ProgressBar` for full list. .. versionadded:: 2.8.0 """ logger.info("Choosing frames to analyze") # if verbose unchanged, use class default verbose = getattr(self, "_verbose", False) if verbose is None else verbose frames = indexed_frames[:, 1] logger.info("Starting preparation") self._prepare_sliced_trajectory(slicer=frames) self._prepare() if len(frames) == 0: # if `frames` were empty in `run` or `stop=0` return self for idx, ts in enumerate(ProgressBar( self._sliced_trajectory, verbose=verbose, **progressbar_kwargs)): self._frame_index = idx # accessed later by subclasses self._ts = ts self.frames[idx] = ts.frame self.times[idx] = ts.time self._single_frame() logger.info("Finishing up") return self
[docs] def _setup_computation_groups( self, n_parts: int, start: int = None, stop: int = None, step: int = None, frames: Union[slice, np.ndarray] = None ) -> list[np.ndarray]: """ Splits the trajectory frames, defined by ``start/stop/step`` or ``frames``, into ``n_parts`` even groups, preserving their indices. Parameters ---------- n_parts : int number of parts to split the workload into start : int, optional start frame stop : int, optional stop frame step : int, optional step size for analysis (1 means to read every frame) frames : array_like, optional array of integers or booleans to slice trajectory; ``frames`` can only be used *instead* of ``start``, ``stop``, and ``step``. Setting *both* ``frames`` and at least one of ``start``, ``stop``, ``step`` to a non-default value will raise a :exc:`ValueError`. Raises ------ ValueError if *both* ``frames`` and at least one of ``start``, ``stop``, or ``frames`` is provided (i.e., set to another value than ``None``) Returns ------- computation_groups : list[np.ndarray] list of (n, 2) shaped np.ndarrays with frame indices and numbers .. versionadded:: 2.8.0 """ if frames is None: start, stop, step = self._trajectory.check_slice_indices(start, stop, step) used_frames = np.arange(start, stop, step) elif not all(opt is None for opt in [start, stop, step]): raise ValueError("start/stop/step cannot be combined with frames") else: used_frames = frames if all(isinstance(obj, bool) for obj in used_frames): arange = np.arange(len(used_frames)) used_frames = arange[used_frames] # similar to list(enumerate(frames)) enumerated_frames = np.vstack([np.arange(len(used_frames)), used_frames]).T if len(enumerated_frames) == 0: return [np.empty((0, 2), dtype=np.int64)] elif len(enumerated_frames) < n_parts: # Issue #4685 n_parts = len(enumerated_frames) warnings.warn(f"Set `n_parts` to {n_parts} to match the total " "number of frames being analyzed") return np.array_split(enumerated_frames, n_parts)
[docs] def _configure_backend( self, backend: Union[str, BackendBase], n_workers: int, unsupported_backend: bool = False ) -> BackendBase: """Matches a passed backend string value with class attributes :attr:`parallelizable` and :meth:`get_supported_backends()` to check if downstream calculations can be performed. Parameters ---------- backend : Union[str, BackendBase] backend to be used: - ``str`` is matched to a builtin backend (one of "serial", "multiprocessing" and "dask") - ``BackendBase`` subclass is checked for the presence of an :meth:`apply` method n_workers : int positive integer with number of workers (processes, in case of built-in backends) to split the work between unsupported_backend : bool, optional if you want to run your custom backend on a parallelizable class that has not been tested by developers, by default ``False`` Returns ------- BackendBase instance of a ``BackendBase`` class that will be used for computations Raises ------ ValueError if :attr:`parallelizable` is set to ``False`` but backend is not ``serial`` ValueError if :attr:`parallelizable` is ``True`` and custom backend instance is used without specifying ``unsupported_backend=True`` ValueError if your trajectory has associated parallelizable transformations but backend is not serial ValueError if ``n_workers`` was specified twice -- in the run() method and durin ``__init__`` of a custom backend ValueError if your backend object instance doesn't have an ``apply`` method .. versionadded:: 2.8.0 """ builtin_backends = { "serial": BackendSerial, "multiprocessing": BackendMultiprocessing, "dask": BackendDask } backend_class = builtin_backends.get(backend, backend) supported_backend_classes = [ builtin_backends.get(b) for b in self.get_supported_backends() ] # check for serial-only classes if not self.parallelizable and backend_class is not BackendSerial: raise ValueError(f"Can not parallelize class {self.__class__}") # make sure user enabled 'unsupported_backend=True' for custom classes if (not unsupported_backend and self.parallelizable and backend_class not in supported_backend_classes): raise ValueError(( f"Must specify 'unsupported_backend=True'" f"if you want to use a custom {backend_class=} for {self.__class__}" )) # check for the presence of parallelizable transformations if backend_class is not BackendSerial and any( not t.parallelizable for t in self._trajectory.transformations): raise ValueError(( "Trajectory should not have " "associated unparallelizable transformations")) # conclude mapping from string to backend class if it's a builtin backend if isinstance(backend, str): return backend_class(n_workers=n_workers) # make sure we haven't specified n_workers twice if ( isinstance(backend, BackendBase) and n_workers is not None and hasattr(backend, 'n_workers') and backend.n_workers != n_workers ): raise ValueError(( f"n_workers specified twice: in {backend.n_workers=}" f"and in run({n_workers=}). Remove it from run()" )) # or pass along an instance of the class itself # after ensuring it has apply method if not isinstance(backend, BackendBase) or not hasattr(backend, "apply"): raise ValueError(( f"{backend=} is invalid: should have 'apply' method " "and be instance of MDAnalysis.analysis.backends.BackendBase" )) return backend
[docs] def run( self, start: int = None, stop: int = None, step: int = None, frames: Iterable = None, verbose: bool = None, n_workers: int = None, n_parts: int = None, backend: Union[str, BackendBase] = None, *, unsupported_backend: bool = False, progressbar_kwargs=None, ): """Perform the calculation Parameters ---------- start : int, optional start frame of analysis stop : int, optional stop frame of analysis step : int, optional number of frames to skip between each analysed frame frames : array_like, optional array of integers or booleans to slice trajectory; ``frames`` can only be used *instead* of ``start``, ``stop``, and ``step``. Setting *both* ``frames`` and at least one of ``start``, ``stop``, ``step`` to a non-default value will raise a :exc:`ValueError`. .. versionadded:: 2.2.0 verbose : bool, optional Turn on verbosity progressbar_kwargs : dict, optional ProgressBar keywords with custom parameters regarding progress bar position, etc; see :class:`MDAnalysis.lib.log.ProgressBar` for full list. Available only for ``backend='serial'`` backend : Union[str, BackendBase], optional By default, performs calculations in a serial fashion. Otherwise, user can choose a backend: ``str`` is matched to a builtin backend (one of ``serial``, ``multiprocessing`` and ``dask``), or a :class:`MDAnalysis.analysis.results.BackendBase` subclass. .. versionadded:: 2.8.0 n_workers : int positive integer with number of workers (processes, in case of built-in backends) to split the work between .. versionadded:: 2.8.0 n_parts : int, optional number of parts to split computations across. Can be more than number of workers. .. versionadded:: 2.8.0 unsupported_backend : bool, optional if you want to run your custom backend on a parallelizable class that has not been tested by developers, by default False .. versionadded:: 2.8.0 .. versionchanged:: 2.2.0 Added ability to analyze arbitrary frames by passing a list of frame indices in the `frames` keyword argument. .. versionchanged:: 2.5.0 Add `progressbar_kwargs` parameter, allowing to modify description, position etc of tqdm progressbars .. versionchanged:: 2.8.0 Introduced ``backend``, ``n_workers``, ``n_parts`` and ``unsupported_backend`` keywords, and refactored the method logic to support parallelizable execution. """ # default to serial execution backend = "serial" if backend is None else backend progressbar_kwargs = {} if progressbar_kwargs is None else progressbar_kwargs if ((progressbar_kwargs or verbose) and not (backend == "serial" or isinstance(backend, BackendSerial))): raise ValueError("Can not display progressbar with non-serial backend") # if number of workers not specified, try getting the number from # the backend instance if possible, or set to 1 if n_workers is None: n_workers = ( backend.n_workers if isinstance(backend, BackendBase) and hasattr(backend, "n_workers") else 1 ) # set n_parts and check that is has a reasonable value n_parts = n_workers if n_parts is None else n_parts # do this as early as possible to check client parameters # before any computations occur executor = self._configure_backend( backend=backend, n_workers=n_workers, unsupported_backend=unsupported_backend) if ( hasattr(executor, "n_workers") and n_parts < executor.n_workers ): # using executor's value here for non-default executors warnings.warn(( f"Analysis not making use of all workers: " f"{executor.n_workers=} is greater than {n_parts=}")) # start preparing the run worker_func = partial( self._compute, progressbar_kwargs=progressbar_kwargs, verbose=verbose) self._setup_frames( trajectory=self._trajectory, start=start, stop=stop, step=step, frames=frames) computation_groups = self._setup_computation_groups( start=start, stop=stop, step=step, frames=frames, n_parts=n_parts ) # get all results from workers in other processes. # we need `AnalysisBase` classes # since they hold `frames`, `times` and `results` attributes remote_objects: list["AnalysisBase"] = executor.apply( worker_func, computation_groups) self.frames = np.hstack([obj.frames for obj in remote_objects]) self.times = np.hstack([obj.times for obj in remote_objects]) # aggregate results from results obtained in remote workers remote_results = [obj.results for obj in remote_objects] results_aggregator = self._get_aggregator() self.results = results_aggregator.merge(remote_results) self._conclude() return self
[docs] def _get_aggregator(self) -> ResultsGroup: """Returns a default aggregator that takes entire results if there is a single object, and raises ValueError otherwise Returns ------- ResultsGroup aggregating object .. versionadded:: 2.8.0 """ return ResultsGroup(lookup=None)
[docs] class AnalysisFromFunction(AnalysisBase): r"""Create an :class:`AnalysisBase` from a function working on AtomGroups Parameters ---------- function : callable function to evaluate at each frame trajectory : MDAnalysis.coordinates.Reader, optional trajectory to iterate over. If ``None`` the first AtomGroup found in args and kwargs is used as a source for the trajectory. *args : list arguments for `function` **kwargs : dict arguments for `function` and :class:`AnalysisBase` Attributes ---------- results.frames : numpy.ndarray simulation frames used in analysis results.times : numpy.ndarray simulation times used in analysis results.timeseries : numpy.ndarray Results for each frame of the wrapped function, stored after call to :meth:`AnalysisFromFunction.run`. Raises ------ ValueError if `function` has the same `kwargs` as :class:`AnalysisBase` Example ------- .. code-block:: python def rotation_matrix(mobile, ref): return mda.analysis.align.rotation_matrix(mobile, ref)[0] rot = AnalysisFromFunction(rotation_matrix, trajectory, mobile, ref).run() print(rot.results.timeseries) .. versionchanged:: 1.0.0 Support for directly passing the `start`, `stop`, and `step` arguments has been removed. These should instead be passed to :meth:`AnalysisFromFunction.run`. .. versionchanged:: 2.0.0 Former :attr:`results` are now stored as :attr:`results.timeseries` .. versionchanged:: 2.8.0 Added :meth:`get_supported_backends()`, introducing 'serial', 'multiprocessing' and 'dask' backends. """ _analysis_algorithm_is_parallelizable = True
[docs] @classmethod def get_supported_backends(cls): return ("serial", "multiprocessing", "dask")
def __init__(self, function, trajectory=None, *args, **kwargs): if (trajectory is not None) and (not isinstance( trajectory, coordinates.base.ProtoReader)): args = (trajectory,) + args trajectory = None if trajectory is None: # all possible places to find trajectory for arg in itertools.chain(args, kwargs.values()): if isinstance(arg, AtomGroup): trajectory = arg.universe.trajectory break if trajectory is None: raise ValueError("Couldn't find a trajectory") self.function = function self.args = args self.kwargs = kwargs super(AnalysisFromFunction, self).__init__(trajectory)
[docs] def _prepare(self): self.results.timeseries = []
[docs] def _get_aggregator(self): return ResultsGroup({"timeseries": ResultsGroup.flatten_sequence})
[docs] def _single_frame(self): self.results.timeseries.append(self.function(*self.args, **self.kwargs))
[docs] def _conclude(self): self.results.frames = self.frames self.results.times = self.times self.results.timeseries = np.asarray(self.results.timeseries)
[docs] def analysis_class(function): r"""Transform a function operating on a single frame to an :class:`AnalysisBase` class. Parameters ---------- function : callable function to evaluate at each frame Attributes ---------- results.frames : numpy.ndarray simulation frames used in analysis results.times : numpy.ndarray simulation times used in analysis results.timeseries : numpy.ndarray Results for each frame of the wrapped function, stored after call to :meth:`AnalysisFromFunction.run`. Raises ------ ValueError if `function` has the same `kwargs` as :class:`AnalysisBase` Examples -------- For use in a library, we recommend the following style .. code-block:: python def rotation_matrix(mobile, ref): return mda.analysis.align.rotation_matrix(mobile, ref)[0] RotationMatrix = analysis_class(rotation_matrix) It can also be used as a decorator .. code-block:: python @analysis_class def RotationMatrix(mobile, ref): return mda.analysis.align.rotation_matrix(mobile, ref)[0] rot = RotationMatrix(u.trajectory, mobile, ref).run(step=2) print(rot.results.timeseries) .. versionchanged:: 2.0.0 Former :attr:`results` are now stored as :attr:`results.timeseries` """ class WrapperClass(AnalysisFromFunction): def __init__(self, trajectory=None, *args, **kwargs): super(WrapperClass, self).__init__(function, trajectory, *args, **kwargs) @classmethod def get_supported_backends(cls): return ("serial", "dask") return WrapperClass
[docs] def _filter_baseanalysis_kwargs(function, kwargs): """ Create two dictionaries with `kwargs` separated for `function` and :class:`AnalysisBase` Parameters ---------- function : callable function to be called kwargs : dict keyword argument dictionary Returns ------- base_args : dict dictionary of AnalysisBase kwargs kwargs : dict kwargs without AnalysisBase kwargs Raises ------ ValueError if `function` has the same `kwargs` as :class:`AnalysisBase` """ try: # pylint: disable=deprecated-method base_argspec = inspect.getfullargspec(AnalysisBase.__init__) except AttributeError: # pylint: disable=deprecated-method base_argspec = inspect.getargspec(AnalysisBase.__init__) n_base_defaults = len(base_argspec.defaults) base_kwargs = { name: val for name, val in zip(base_argspec.args[-n_base_defaults:], base_argspec.defaults) } try: # pylint: disable=deprecated-method argspec = inspect.getfullargspec(function) except AttributeError: # pylint: disable=deprecated-method argspec = inspect.getargspec(function) for base_kw in base_kwargs.keys(): if base_kw in argspec.args: raise ValueError( "argument name '{}' clashes with AnalysisBase argument." "Now allowed are: {}".format(base_kw, base_kwargs.keys()) ) base_args = {} for argname, default in base_kwargs.items(): base_args[argname] = kwargs.pop(argname, default) return base_args, kwargs