Binned and unbinned signals#

Signals that are a histogram of a probability density function (pdf) should have the is_binned attribute of the signal axis set to True. The reason is that some methods operate differently on signals that are binned. An example of binned signals are EDS spectra, where the multichannel analyzer integrates the signal counts in every channel (=bin). Note that for 2D signals each signal axis has an is_binned attribute that can be set independently. For example, for the first signal axis: signal.axes_manager.signal_axes[0].is_binned.

The default value of the is_binned attribute is shown in the following table:

Binned default values for the different subclasses.#
BaseSignal subclass	binned	Library
`BaseSignal`	False	hyperspy
`Signal1D`	False	hyperspy
`exspy.signals.EELSSpectrum`	True	exSpy
`exspy.signals.EDSSEMSpectrum`	True	exSpy
`exspy.signals.EDSTEMSpectrum`	True	exSpy
`Signal2D`	False	hyperspy
`ComplexSignal`	False	hyperspy
`ComplexSignal1D`	False	hyperspy
`ComplexSignal2D`	False	hyperspy

To change the default value:

>>> s.axes_manager[-1].is_binned = True 

Changed in version 1.7: The binned attribute from the metadata has been replaced by the axis attributes is_binned.

Integration of binned signals#

For binned axes, the detector already provides the per-channel integration of the signal. Therefore, in this case, integrate1D() performs a simple summation along the given axis. In contrast, for unbinned axes, integrate1D() calls the integrate_simpson() method.