remove_dynamic_background#
- EBSD.remove_dynamic_background(operation: str = 'subtract', filter_domain: str = 'frequency', std: Union[None, int, float] = None, truncate: Union[int, float] = 4.0, show_progressbar: Optional[bool] = None, inplace: bool = True, lazy_output: Optional[bool] = None, **kwargs) Union[None, EBSD, LazyEBSD][source]#
Remove the dynamic background.
The removal is performed by subtracting or dividing by a Gaussian blurred version of each pattern. Resulting pattern intensities are rescaled to fill the input patterns’ data type range individually.
- Parameters:
- operation
Whether to
"subtract"(default) or"divide"by the dynamic background pattern.- filter_domain
Whether to obtain the dynamic background by applying a Gaussian convolution filter in the
"frequency"(default) or"spatial"domain.- std
Standard deviation of the Gaussian window. If None (default), it is set to width/8.
- truncate
Truncate the Gaussian window at this many standard deviations. Default is
4.0.- show_progressbar
Whether to show a progressbar. If not given, the value of
hyperspy.api.preferences.General.show_progressbaris used.- inplace
Whether to operate on the current signal or return a new one. Default is
True.- lazy_output
Whether the returned signal is lazy. If not given this follows from the current signal. Can only be
Trueifinplace=False.- **kwargs
Keyword arguments passed to the Gaussian blurring function determined from
filter_domain.
- Returns:
s_outBackground corrected signal, returned if
inplace=False. Whether it is lazy is determined fromlazy_output.
See also
Examples
Remove the static and dynamic background
>>> import kikuchipy as kp >>> s = kp.data.nickel_ebsd_small() >>> s.remove_static_background() >>> s.remove_dynamic_background(operation="divide", std=5)
