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.
Deprecated since version 1.3:
It will be removed in 2.0. Use
instead, possibly in combination with a Region Of Interest (ROI) if interactivity
The following methods (that include user interfaces when no arguments are passed) can perform data smoothing with different algorithms:
statsmodelsto be installed)
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
- Interpolate the spectra in between two positions
- Convolve the spectra with a gaussian
- Apply a hanning taper to the spectra