PolarDiffraction2D#

class pyxem.signals.PolarDiffraction2D(*args, **kwargs)[source]#

Bases: CommonDiffraction, Signal2D

Signal class for two-dimensional diffraction data in polar coordinates.

Parameters:

*args – See Signal2D.
**kwargs – See Signal2D

Attributes

Methods

`PolarDiffraction2D.get_angular_correlation`([...])	Calculate the angular auto-correlation function in the form of a Signal2D class.
`PolarDiffraction2D.get_angular_power`([mask, ...])	Calculate the power spectrum of the angular auto-correlation function in the form of a Signal2D class.
`PolarDiffraction2D.get_full_pearson_correlation`([...])	Calculate the fully convolved pearson rotational correlation in the form of a Signal1D class.
`PolarDiffraction2D.get_orientation`(simulation)	Match the orientation with some simulated diffraction patterns using an accelerated orientation mapping algorithm. The details of the algorithm are described in the paper: "Free, flexible and fast: Orientation mapping using the multi-core and GPU-accelerated template matching capabilities in the python-based open source 4D-STEM analysis toolbox Pyxem" :Parameters: * simulation (DiffractionSimulation) -- The diffraction simulation object to use for indexing. * n_keep (int) -- The number of orientations to keep for each diffraction pattern. * frac_keep (float) -- The fraction of the best matching orientations to keep. * n_best (int) -- The number of best matching orientations to return. If n_best == -1 all of the orientations and correlations are returned. * normalize_templates (bool) -- Normalize the templates to the same intensity.. * kwargs (dict) -- Any additional options for the `map()` function.
`PolarDiffraction2D.get_pearson_correlation`(...)	[Deprecated]
`PolarDiffraction2D.get_resolved_pearson_correlation`([...])	Calculate the pearson rotational correlation with k resolution in the form of a Signal2D class.
`PolarDiffraction2D.subtract_diffraction_background`([...])	Background subtraction of the diffraction data.