Signal2D Tools#

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

Signal registration and alignment#

The align2D() and estimate_shift2D() methods provide advanced image alignment functionality.

# Estimate shifts, then align the images
>>> shifts = s.estimate_shift2D() # doctest: +SKIP
>>> s.align2D(shifts=shifts) # doctest: +SKIP

# Estimate and align in a single step
>>> s.align2D() # doctest: +SKIP

Warning

s.align2D() will modify the data in-place. If you don’t want to modify your original data, first take a copy before aligning.

Sub-pixel accuracy can be achieved in two ways:

scikit-image’s upsampled matrix-multiplication DFT method [Guizar2008], by setting the sub_pixel_factor keyword argument
for multi-dimensional datasets only, using the statistical method [Schaffer2004], by setting the reference keyword argument to "stat"

# skimage upsampling method
>>> shifts = s.estimate_shift2D(sub_pixel_factor=20) # doctest: +SKIP

# stat method
>>> shifts = s.estimate_shift2D(reference="stat") # doctest: +SKIP

# combined upsampling and statistical method
>>> shifts = s.estimate_shift2D(reference="stat", sub_pixel_factor=20) # doctest: +SKIP

If you have a large stack of images, the image alignment is automatically done in parallel.

You can control the number of threads used with the num_workers argument. Or by adjusting the scheduler.

# Estimate shifts
>>> shifts = s.estimate_shift2D() # doctest: +SKIP

# Align images in parallel using 4 threads
>>> s.align2D(shifts=shifts, num_workers=4) # doctest: +SKIP

Cropping a Signal2D#

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

>>> im = hs.data.wave_image()
>>> im.crop_signal(left=0.5, top=0.7, bottom=2.0) # im is cropped in-place

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

>>> im = hs.data.wave_image()
>>> # im is not cropped, imc is a "cropped view" of im
>>> imc = im.isig[0.5:, 0.7:2.0]

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

>>> im = hs.data.wave_image()
>>> roi = hs.roi.RectangularROI()
>>> im.plot()
>>> imc = roi.interactive(im)
>>> imc.plot()

../_images/interactive_signal2d_cropping.png — Interactive image cropping using a ROI.#

Interactive calibration#

The scale can be calibrated interactively by using calibrate(), which is used to set the scale by dragging a line across some feature of known size.

>>> s = hs.signals.Signal2D(np.random.random((200, 200)))
>>> s.calibrate() 

The same function can also be used non-interactively.

>>> s = hs.signals.Signal2D(np.random.random((200, 200)))
>>> s.calibrate(x0=1, y0=1, x1=5, y1=5, new_length=3.4, units="nm", interactive=False)

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.

Peak finding#

Added in version 1.6.

The find_peaks() method provides access to a number of algorithms for peak finding in two dimensional signals. The methods available are:

Maximum based peak finder#

>>> s.find_peaks(method='local_max') 
>>> s.find_peaks(method='max') 
>>> s.find_peaks(method='minmax') 

These methods search for peaks using maximum (and minimum) values in the image. There all have a distance parameter to set the minimum distance between the peaks.

the 'local_max' method uses the skimage.feature.peak_local_max() function (distance and threshold parameters are mapped to min_distance and threshold_abs, respectively).
the 'max' method uses the find_peaks_max() function to search for peaks higher than alpha * sigma, where alpha is parameters and sigma is the standard deviation of the image. It also has a distance parameters to set the minimum distance between peaks.
the 'minmax' method uses the find_peaks_minmax() function to locate the positive peaks in an image by comparing maximum and minimum filtered images. Its threshold parameter defines the minimum difference between the maximum and minimum filtered images.

Zaeferrer peak finder#

>>> s.find_peaks(method='zaefferer') 

This algorithm was developed by Zaefferer [Zaefferer2000]. It is based on a gradient threshold followed by a local maximum search within a square window, which is moved until it is centered on the brightest point, which is taken as a peak if it is within a certain distance of the starting point. It uses the find_peaks_zaefferer() function, which can take grad_threshold, window_size and distance_cutoff as parameters. See the find_peaks_zaefferer() function documentation for more details.

Ball statistical peak finder#

>>> s.find_peaks(method='stat') 

Described by White [White2009], this method is based on finding points that have a statistically higher value than the surrounding areas, then iterating between smoothing and binarising until the number of peaks has converged. This method can be slower than the others, but is very robust to a variety of image types. It uses the find_peaks_stat() function, which can take alpha, window_radius and convergence_ratio as parameters. See the find_peaks_stat() function documentation for more details.

Matrix based peak finding#

>>> s.find_peaks(method='laplacian_of_gaussians') 
>>> s.find_peaks(method='difference_of_gaussians') 

These methods are essentially wrappers around the Laplacian of Gaussian (skimage.feature.blob_log()) or the difference of Gaussian (skimage.feature.blob_dog()) methods, based on stacking the Laplacian/difference of images convolved with Gaussian kernels of various standard deviations. For more information, see the example in the scikit-image documentation.

Template matching#

>>> x, y = np.meshgrid(np.arange(-2, 2.5, 0.5), np.arange(-2, 2.5, 0.5))
>>> template = hs.model.components2D.Gaussian2D().function(x, y)
>>> s.find_peaks(method='template_matching', template=template, interactive=False)
<BaseSignal, title: , dimensions: (|ragged)>

This method locates peaks in the cross correlation between the image and a template using the find_peaks_xc() function. See the find_peaks_xc() function documentation for more details.

Interactive parametrization#

Many of the peak finding algorithms implemented here have a number of tunable parameters that significantly affect their accuracy and speed. The GUIs can be used to set to select the method and set the parameters interactively:

>>> s.find_peaks(interactive=True) 

Several widgets are available:

The method selector is used to compare different methods. The last-set parameters are maintained.
The parameter adjusters will update the parameters of the method and re-plot the new peaks.

Note

Some methods take significantly longer than others, particularly where there are a large number of peaks to be found. The plotting window may be inactive during this time.