The methods described in this section are only available for one-dimensional signals in the Signal1D class.
crop_signal1D() crops the
spectral energy range in-place. If no parameter is passed, a user interface
appears in which to crop the one dimensional signal. For example:
s = hs.datasets.example_signals.EDS_TEM_Spectrum() s.crop_signal1D(5, 15) # s is cropped in place
Additionally, cropping in HyperSpy can be performed using the Signal indexing syntax. For example, the following crops a spectrum to the 5 keV-15 keV region:
s = hs.datasets.example_signals.EDS_TEM_Spectrum() sc = s.isig[5.:15.] # s is not cropped, sc is a "cropped view" of s
It is possible to crop interactively using Region Of Interest (ROI). For example:
s = hs.datasets.example_signals.EDS_TEM_Spectrum() roi = hs.roi.SpanROI(left=5, right=15) s.plot() sc = roi.interactive(s)
New in version 1.4:
plot_remainder keyword arguments and big speed
remove_background() method provides
background removal capabilities through both a CLI and a GUI. The GUI displays
an interactive preview of the remainder after background subtraction. Current
background type supported are power law, offset, polynomial and gaussian.
By default the background parameters are estimated using analytical approximations
fast=True). For better accuracy, but higher processing
time, the parameters can be estimated by curve fitting by setting
Example of usage:
s = hs.datasets.artificial_data.get_core_loss_eels_signal(add_powerlaw=True) s.remove_background(zero_fill=False)
calibrate() method provides a user
interface to calibrate the spectral axis.
The following methods use sub-pixel cross-correlation or user-provided shifts to align spectra. They support applying the same transformation to multiple files.
The following methods (that include user interfaces when no arguments are passed) can perform data smoothing with different algorithms:
A peak finding routine based on the work of T. O’Haver is available in HyperSpy