HyperSpy API has changed in version 2.0, see the release notes!


class hyperspy.api.signals.Signal2D(*args, **kwargs)#

Bases: BaseSignal, CommonSignal2D

General 2D signal class.

Create a signal instance.


The signal data. It can be an array of any dimensions.

axes[dict/axes], optional

List of either dictionaries or axes objects to define the axes (see the documentation of the AxesManager class for more details).

attributesdict, optional

A dictionary whose items are stored as attributes.

metadatadict, optional

A dictionary containing a set of parameters that will to stores in the metadata attribute. Some parameters might be mandatory in some cases.

original_metadatadict, optional

A dictionary containing a set of parameters that will to stores in the original_metadata attribute. It typically contains all the parameters that has been imported from the original data file.

raggedbool or None, optional

Define whether the signal is ragged or not. Overwrite the ragged value in the attributes dictionary. If None, it does nothing. Default is None.

add_ramp(ramp_x, ramp_y, offset=0)#

Add a linear ramp to the signal.

ramp_x: float

Slope of the ramp in x-direction.

ramp_y: float

Slope of the ramp in y-direction.

offset: float, optional

Offset of the ramp at the signal fulcrum.


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 axes_manager of the signal.

align2D(crop=True, fill_value=nan, shifts=None, expand=False, interpolation_order=1, show_progressbar=None, num_workers=None, **kwargs)#

Align the images in-place using scipy.ndimage.shift().

The images can be aligned using either user-provided shifts or by first estimating the shifts.

See estimate_shift2D() for more details on estimating image shifts.


If True, the data will be cropped not to include regions with missing data

fill_valueint, float, numpy.nan

The areas with missing data are filled with the given value. Default is np.nan.

shiftsNone or numpy.ndarray

The array of shifts must be in pixel units. The shape must be the navigation shape using numpy order convention. If None the shifts are estimated using estimate_shift2D().


If True, the data will be expanded to fit all data after alignment. Overrides crop.

interpolation_order: int

The order of the spline interpolation. Default is 1, linear interpolation.

show_progressbarNone or bool

If True, display a progress bar. If None, the default from the preferences settings is used.

num_workersNone or int

Number of worker used by dask. If None, default to dask default value.


Keyword arguments passed to estimate_shift2D().


The estimated shifts are returned only if shifts is None


If one of the signal axes is a non-uniform axis.

See also

calibrate(x0=None, y0=None, x1=None, y1=None, new_length=None, units=None, interactive=True, display=True, toolkit=None)#

Calibrate the x and y signal dimensions.

Can be used either interactively, or by passing values as parameters.

x0, y0, x1, y1float, int, optional

If interactive is False, these must be set. If given in floats the input will be in scaled axis values. If given in integers, the input will be in non-scaled pixel values. Similar to how integer and float input works when slicing using isig and inav.

new_lengthscalar, optional

If interactive is False, this must be set.

unitsstr, optional

If interactive is False, this is used to set the axes units.

interactivebool, default True

If True, will use a plot with an interactive line for calibration. If False, x0, y0, x1, y1 and new_length must be set.

displaybool, default True
toolkitstr, optional


>>> s = hs.signals.Signal2D(np.random.random((100, 100)))
>>> s.calibrate() 

Running non-interactively

>>> s = hs.signals.Signal2D(np.random.random((100, 100)))
>>> s.calibrate(x0=10, y0=10, x1=60, y1=10, new_length=100,
...     interactive=False, units="nm")

Create a model for the current signal

dictionaryNone or dict, optional

A dictionary to be used to recreate a model. Usually generated using hyperspy.model.BaseModel.as_dictionary()

crop_signal(top=None, bottom=None, left=None, right=None, convert_units=False)#

Crops in signal space and in place.

top, bottom, left, rightint or float

If int the values are taken as indices. If float the values are converted to indices.


Default is False If True, convert the signal units using the ‘convert_to_units’ method of the axes_manager. If False, does nothing.

estimate_shift2D(reference='current', correlation_threshold=None, chunk_size=30, roi=None, normalize_corr=False, sobel=True, medfilter=True, hanning=True, plot=False, dtype='float', show_progressbar=None, sub_pixel_factor=1)#

Estimate the shifts in an image using phase correlation.

This method can only estimate the shift by comparing bi-dimensional features that should not change position between frames. To decrease the memory usage, the time of computation and the accuracy of the results it is convenient to select a region of interest by setting the roi argument.

reference{‘current’, ‘cascade’ ,’stat’}

If ‘current’ (default) the image at the current coordinates is taken as reference. If ‘cascade’ each image is aligned with the previous one. If ‘stat’ the translation of every image with all the rest is estimated and by performing statistical analysis on the result the translation is estimated.

correlation_thresholdNone, str or float

This parameter is only relevant when reference=’stat’. If float, the shift estimations with a maximum correlation value lower than the given value are not used to compute the estimated shifts. If ‘auto’ the threshold is calculated automatically as the minimum maximum correlation value of the automatically selected reference image.

chunk_sizeNone or int

If int and reference=’stat’ the number of images used as reference are limited to the given value.

roituple of int or float

Define the region of interest (left, right, top, bottom). If int (float), the position is given by axis index (value). Note that ROIs can be used in place of a tuple.

normalize_corrbool, default False

If True, use phase correlation to align the images, otherwise use cross correlation.

sobelbool, default True

Apply a Sobel filter for edge enhancement

medfilterbool, default True

Apply a median filter for noise reduction

hanningbool, default True

Apply a 2D hanning filter

plotbool or str

If True plots the images after applying the filters and the phase correlation. If ‘reuse’, it will also plot the images, but it will only use one figure, and continuously update the images in that figure as it progresses through the stack.

dtypestr or numpy.dtype

Typecode or data-type in which the calculations must be performed.

show_progressbarNone or bool

If True, display a progress bar. If None, the default from the preferences settings is used.


Estimate shifts with a sub-pixel accuracy of 1/sub_pixel_factor parts of a pixel. Default is 1, i.e. no sub-pixel accuracy.


Estimated shifts in pixels.

See also



The statistical analysis approach to the translation estimation when using reference='stat' roughly follows [*]. If you use it please cite their article.


find_peaks(method='local_max', interactive=True, current_index=False, show_progressbar=None, num_workers=None, display=True, toolkit=None, get_intensity=False, **kwargs)#

Find peaks in a 2D signal.

Function to locate the positive peaks in an image using various, user specified, methods. Returns a structured array containing the peak positions.


Select peak finding algorithm to implement. Available methods are:

  • ‘local_max’ - simple local maximum search using the skimage.feature.peak_local_max() function

  • ‘max’ - simple local maximum search using the find_peaks_max().

  • ‘minmax’ - finds peaks by comparing maximum filter results with minimum filter, calculates centers of mass. See the find_peaks_minmax() function.

  • ‘zaefferer’ - based on gradient thresholding and refinement by local region of interest optimisation. See the find_peaks_zaefferer() function.

  • ‘stat’ - based on statistical refinement and difference with respect to mean intensity. See the find_peaks_stat() function.

  • ‘laplacian_of_gaussian’ - a blob finder using the laplacian of Gaussian matrices approach. See the find_peaks_log() function.

  • ‘difference_of_gaussian’ - a blob finder using the difference of Gaussian matrices approach. See the find_peaks_dog() function.

  • ‘template_matching’ - A cross correlation peakfinder. This method requires providing a template with the template parameter, which is used as reference pattern to perform the template matching to the signal. It uses the skimage.feature.match_template() function and the peaks position are obtained by using minmax method on the template matching result.


If True, the method parameter can be adjusted interactively. If False, the results will be returned.


If True, the computation will be performed for the current index.


If True, the intensity of the peak will be returned as an additional column, the last one.

show_progressbarNone or bool

If True, display a progress bar. If None, the default from the preferences settings is used.

num_workersNone or int

Number of worker used by dask. If None, default to dask default value.


If True, display the user interface widgets. If False, return the widgets container in a dictionary, usually for customisation or testing.

toolkitstr, iterable of str or None

If None (default), all available widgets are displayed or returned. If string, only the widgets of the selected toolkit are displayed if available. If an interable of toolkit strings, the widgets of all listed toolkits are displayed or returned.


Keywords parameters associated with above methods, see the documentation of each method for more details.

peaksBaseSignal or numpy.ndarray

numpy.ndarray if current_index=True. Ragged signal with shape (npeaks, 2) that contains the x, y pixel coordinates of peaks found in each image sorted first along y and then along x.


As a convenience, the ‘local_max’ method accepts the ‘distance’ and ‘threshold’ argument, which will be map to the ‘min_distance’ and ‘threshold_abs’ of the skimage.feature.peak_local_max() function.

plot(navigator='auto', plot_markers=True, autoscale='v', norm='auto', vmin=None, vmax=None, gamma=1.0, linthresh=0.01, linscale=0.1, scalebar=True, scalebar_color='white', axes_ticks=None, axes_off=False, axes_manager=None, no_nans=False, colorbar=True, centre_colormap='auto', min_aspect=0.1, navigator_kwds={}, **kwargs)#

Plot the signal at the current coordinates.

For multidimensional datasets an optional figure, the “navigator”, with a cursor to navigate that data is raised. In any case it is possible to navigate the data using the sliders. Currently only signals with signal_dimension equal to 0, 1 and 2 can be plotted.

navigatorstr, None, or BaseSignal (or subclass).
Allowed string values are ``’auto’``, ``’slider’``, and ``’spectrum’``.
  • If 'auto':

    • If navigation_dimension > 0, a navigator is provided to explore the data.

    • If navigation_dimension is 1 and the signal is an image the navigator is a sum spectrum obtained by integrating over the signal axes (the image).

    • If navigation_dimension is 1 and the signal is a spectrum the navigator is an image obtained by stacking all the spectra in the dataset horizontally.

    • If navigation_dimension is > 1, the navigator is a sum image obtained by integrating the data over the signal axes.

    • Additionally, if navigation_dimension > 2, a window with one slider per axis is raised to navigate the data.

    • For example, if the dataset consists of 3 navigation axes “X”, “Y”, “Z” and one signal axis, “E”, the default navigator will be an image obtained by integrating the data over “E” at the current “Z” index and a window with sliders for the “X”, “Y”, and “Z” axes will be raised. Notice that changing the “Z”-axis index changes the navigator in this case.

    • For lazy signals, the navigator will be calculated using the compute_navigator() method.

  • If 'slider':

    • If navigation dimension > 0 a window with one slider per axis is raised to navigate the data.

  • If 'spectrum':

    • If navigation_dimension > 0 the navigator is always a spectrum obtained by integrating the data over all other axes.

    • Not supported for lazy signals, the 'auto' option will be used instead.

  • If None, no navigator will be provided.

Alternatively a BaseSignal (or subclass) instance can be provided. The navigation or signal shape must match the navigation shape of the signal to plot or the navigation_shape + signal_shape must be equal to the navigator_shape of the current object (for a dynamic navigator). If the signal dtype is RGB or RGBA this parameter has no effect and the value is always set to 'slider'.

axes_managerNone or AxesManager

If None, the signal’s axes_manager attribute is used.

plot_markersbool, default True

Plot markers added using s.add_marker(marker, permanent=True). Note, a large number of markers might lead to very slow plotting.


Only for image navigator, additional keyword arguments for matplotlib.pyplot.imshow().

colorbarbool, optional

If true, a colorbar is plotted for non-RGB images.

autoscalestr, optional

The string must contain any combination of the 'x', 'y' and 'v' characters. If 'x' or 'y' are in the string, the corresponding axis limits are set to cover the full range of the data at a given position. If 'v' (for values) is in the string, the contrast of the image will be set automatically according to vmin` and ``vmax when the data or navigation indices change. Default is 'v'.

normstr {"auto"` | ``"linear" | "power" | "log" | "symlog"} or matplotlib.colors.Normalize

Set the norm of the image to display. If "auto", a linear scale is used except if when power_spectrum=True in case of complex data type. "symlog" can be used to display negative value on a negative scale - read matplotlib.colors.SymLogNorm and the linthresh and linscale parameter for more details.

vmin, vmax{scalar, str}, optional

vmin and vmax are used to normalise the displayed data. It can be a float or a string. If string, it should be formatted as 'xth', where 'x' must be an float in the [0, 100] range. 'x' is used to compute the x-th percentile of the data. See numpy.percentile() for more information.

gammafloat, optional

Parameter used in the power-law normalisation when the parameter norm="power". Read matplotlib.colors.PowerNorm for more details. Default value is 1.0.

linthreshfloat, optional

When used with norm="symlog", define the range within which the plot is linear (to avoid having the plot go to infinity around zero). Default value is 0.01.

linscalefloat, optional

This allows the linear range (-linthresh to linthresh) to be stretched relative to the logarithmic range. Its value is the number of powers of base to use for each half of the linear range. See matplotlib.colors.SymLogNorm for more details. Defaulf value is 0.1.

scalebarbool, optional

If True and the units and scale of the x and y axes are the same a scale bar is plotted.

scalebar_colorstr, optional

A valid MPL color string; will be used as the scalebar color.

axes_ticks{None, bool}, optional

If True, plot the axes ticks. If None axes_ticks are only plotted when the scale bar is not plotted. If False the axes ticks are never plotted.

axes_offbool, default False
no_nansbool, optional

If True, set nans to zero for plotting.

centre_colormapbool or "auto"

If True the centre of the color scheme is set to zero. This is specially useful when using diverging color schemes. If “auto” (default), diverging color schemes are automatically centred.

min_aspectfloat, optional

Set the minimum aspect ratio of the image and the figure. To keep the image in the aspect limit the pixels are made rectangular.


Only when plotting an image: additional (optional) keyword arguments for matplotlib.pyplot.imshow().