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)
remove_background() method provides
background removal capabilities through both a CLI and a GUI. Current
background type supported are power law, offset, polynomial and gaussian.
By default the background is estimated, but a full fit can also be used.
The full fit is more accurate, but slower.
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:
New in version 0.5.
spikes_removal_tool() provides an user
interface to remove spikes from spectra.
A peak finding routine based on the work of T. O’Haver is available in HyperSpy