NumbaAnalysisBase¶

class mdcraft.analysis.base.NumbaAnalysisBase(trajectory: ReaderBase, verbose: bool = False, **kwargs)[source]¶

Bases: SerialAnalysisBase

A Numba-accelerated analysis base object.

Parameters:

trajectoryMDAnalysis.coordinates.base.ReaderBase: Simulation trajectory.
verbosebool, default: True: Determines whether detailed progress is shown.
**kwargs: Additional keyword arguments to pass to MDAnalysis.analysis.base.AnalysisBase.

Methods

`get_supported_backends`	Tuple with backends supported by the core library for a given class.
`run`	Performs the calculation.
`save`	Saves results to a binary or archive file in NumPy format.

classmethod get_supported_backends()¶

Tuple with backends supported by the core library for a given class. User can pass either one of these values as backend=... to run() method, or a custom object that has apply method (see documentation for 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 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 run(backend=...)()

Added in version 2.8.0: ..

property parallelizable¶

Boolean mark showing that a given class can be parallelizable with split-apply-combine procedure. Namely, if we can safely distribute _single_frame() to multiple workers and then combine them with a proper _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 _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

Added in version 2.8.0: ..

run(start: int = None, stop: int = None, step: int = None, frames: slice | ndarray[int] = None, verbose: bool = None, *, n_threads: int = None, **kwargs) → NumbaAnalysisBase[source]¶

Performs the calculation.

Parameters:

startint, optional: Starting frame for analysis.
stopint, optional: Ending frame for analysis.
stepint, optional: Number of frames to skip between each analyzed frame.
framesslice or array-like, optional: Index or logical array of the desired trajectory frames.
verbosebool, optional: Determines whether detailed progress is shown.
n_threadsint, keyword-only, optional: Number of threads to use for analysis.
**kwargs: Additional keyword arguments to pass to MDAnalysis.lib.log.ProgressBar.

Returns:

selfNumbaAnalysisBase: Analysis object with results.

save(file: str | TextIO, archive: bool = True, compress: bool = True, **kwargs) → None¶

Saves results to a binary or archive file in NumPy format.

Parameters:

filestr or file: Filename or file-like object where the data will be saved. If file is a str, the .npy or .npz extension will be appended automatically if not already present.
archivebool, default: True: Determines whether the results are saved to a single archive file. If True, the data is stored in a .npz file. Otherwise, the data is saved to multiple .npy files.
compressbool, default: True: Determines whether the .npz file is compressed. Has no effect when archive=False.
**kwargs: Additional keyword arguments to pass to numpy.save(), numpy.savez(), or numpy.savez_compressed(), depending on the values of archive and compress.