get_diffraction_pattern#
- Simulation2D.get_diffraction_pattern(shape: Tuple[int, int] = None, sigma: float = 10, direct_beam_position: Tuple[int, int] = None, in_plane_angle: float = 0, calibration: float = 0.01, mirrored: bool = False, fast: bool = True, normalize: bool = True, clip_threshold: float = 1)[source]#
Returns the diffraction data as a numpy array with two-dimensional Gaussians representing each diffracted peak. Should only be used for qualitative work.
- Parameters:
- shape
tupleofints The size of a side length (in pixels)
- sigma
float Standard deviation of the Gaussian function to be plotted (in pixels).
- direct_beam_position: 2-tuple of ints, optional
The (x,y) coordinate in pixels of the direct beam. Defaults to the center of the image.
- in_plane_angle: float, optional
In plane rotation of the pattern in degrees
- mirrored: bool, optional
Whether the pattern should be flipped over the x-axis, corresponding to the inverted orientation
- fast: bool, optional
Whether to speed up calculations by rounding spot coordinates down to integer pixel
- normalize: bool, optional
Whether to normalize the pattern to values between 0 and 1
- clip_threshold: float, optional
Only used when fast is False. Pixel intensity threshold, such that pixels which would be below this value are ignored. Thresholding performed before possible normalization. See diffsims.pattern.detector_functions.get_pattern_from_pixel_coordinates_and_intensities for details.
- Returns
- ——-
- diffraction-pattern
numpy.array The simulated electron diffraction pattern, normalized by default.
- shape
Notes
If don’t know the exact calibration of your diffraction signal using 1e-2 produces reasonably good patterns when the lattice parameters are on the order of 0.5nm and the default size and sigma are used.
