Signal1D Tools#

The methods described in this section are only available for one-dimensional signals in the Signal1D class.

Cropping#

The crop_signal() crops the the signal object along the signal axis (e.g. 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.data.two_gaussians()
>>> s.crop_signal(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 signal to the 5.0-15.0 region:

>>> s = hs.data.two_gaussians()
>>> 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.data.two_gaussians()
>>> roi = hs.roi.SpanROI(left=5, right=15)
>>> s.plot()
>>> sc = roi.interactive(s)

../_images/interactive_signal1d_cropping.png — Interactive spectrum cropping using a ROI.#

Background removal#

Added in version 1.4: zero_fill and plot_remainder keyword arguments and big speed improvements.

The 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. Currently, the following background types are supported: Doniach, Exponential, Gaussian, Lorentzian, Polynomial, Power law (default), Offset, Skew normal, Split Voigt and Voigt. By default, the background parameters are estimated using analytical approximations (keyword argument fast=True). The fast option is not accurate for most background types - except Gaussian, Offset and Power law - but it is useful to estimate the initial fitting parameters before performing a full fit. For better accuracy, but higher processing time, the parameters can be estimated using curve fitting by setting fast=False.

Example of usage:

>>> s = exspy.data.EELS_MnFe(add_powerlaw=True) 
>>> s.remove_background() 

../_images/signal_1d_remove_background.png — Interactive background removal. In order to select the region used to estimate the background parameters (red area in the figure) click inside the axes of the figure and drag to the right without releasing the button.#

Calibration#

The calibrate() method provides a user interface to calibrate the spectral axis.

Alignment#

The following methods use sub-pixel cross-correlation or user-provided shifts to align spectra. They support applying the same transformation to multiple files.

Integration#

To integrate signals use the integrate1D() method. Possibly in combination with a Region Of Interest (ROI) if interactivity is required. Otherwise, a signal subrange for integration can also be chosen with the isig method.

>>> s.isig[0.2:0.5].integrate1D(axis=0) 

Data smoothing#

The following methods (that include user interfaces when no arguments are passed) can perform data smoothing with different algorithms:

smooth_lowess() (requires statsmodels to be installed)
smooth_tv()
smooth_savitzky_golay()

Spike removal#

spikes_removal_tool() provides an user interface to remove spikes from spectra. The derivative histogram allows to identify the appropriate threshold. It is possible to use this tool on a specific interval of the data by slicing the data. For example, to use this tool in the signal between indices 8 and 17:

>>> s = hs.signals.Signal1D(np.arange(5*10*20).reshape((5, 10, 20)))
>>> s.isig[8:17].spikes_removal_tool() 

The options navigation_mask or signal_mask provide more flexibility in the selection of the data, but these require a mask (booleen array) as parameter, which needs to be created manually:

>>> s = hs.signals.Signal1D(np.arange(5*10*20).reshape((5, 10, 20)))

To get a signal mask, get the mean over the navigation space

>>> s_mean = s.mean()
>>> mask = s_mean > 495
>>> s.spikes_removal_tool(signal_mask=mask) 

../_images/spikes_removal_tool.png — Spikes removal tool.#

Peak finding#

A peak finding routine based on the work of T. O’Haver is available in HyperSpy through the find_peaks1D_ohaver() method.

Estimate peak width#

For asymmetric peaks, fitted functions <model.fitting> may not provide an accurate description of the peak, in particular the peak width. The function estimate_peak_width() determines the width of a peak at a certain fraction of its maximum value.

Other methods#

Interpolate the spectra in between two positions interpolate_in_between()
Convolve the spectra with a gaussian gaussian_filter()
Apply a hanning taper to the spectra hanning_taper()