Signal2D Tools

The methods described in this section are only available for two-dimensional signals in the Signal2D class.

Two dimensional signal registration (alignment)

New in version 1.4: sub_pixel_factor keyword.

The align2D() and estimate_shift2D() methods provide advanced image alignment functionality. Sub-pixel accuracy can be achieved by using skimage’s upsampled matrix-multiplication DFT method [Guizar2008]—by setting the sub_pixel_factor keyword argument— and/or, for multi-dimensional datasets only, using the statistical method [Schaffer2004]—by setting the reference keyword argument to "stat".

Cropping an image

The crop_image() method crops the image in-place e.g.:

>>> im = hs.datasets.example_signals.object_hologram()
>>> imc = im.crop(left=120, top=300, bottom=560) # im is cropped in-place

Cropping in HyperSpy is performed using the Signal indexing syntax. For example, to crop an image:

>>> im = hs.datasets.example_signals.object_hologram()
>>> # im is not cropped, imc is a "cropped view" of im
>>> imc = im.isig[120.:, 300.:560.]

It is possible to crop interactively using Region Of Interest (ROI). For example:

>>> im = hs.datasets.example_signals.object_hologram()
>>> roi = hs.roi.RectangularROI(left=120, right=460., top=300, bottom=560)
>>> im.plot()
>>> imc = roi.interactive(im)
>>> imc.plot()

Interactive image cropping using a ROI.

Add a linear ramp

A linear ramp can be added to the signal via the add_ramp() method. The parameters ramp_x and ramp_y dictate the slope of the ramp in x- and y direction, while the offset is determined by the offset parameter. The fulcrum of the linear ramp is at the origin and the slopes are given in units of the axis with the according scale taken into account. Both are available via the AxesManager of the signal.