PolarDiffraction2D#

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

Bases: CommonDiffraction, Signal2D

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

Parameters:

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.