Working with streak images

LumiSpy has implemented handling of streak camera images, which are a special subclass of hyperspy.api.signals.Signal2D as the two signal axes have different units: Namely, wavelength (or energy) and time. The signal class is LumiTransientSpectrum. An important feature is that also for streak camera images (or linescans/maps of these), the wavelength axis can be transformed to e.g. a The energy axis.

Note

For streak images, not all tools available for hyperspy.api.signals.Signal2D will work. However, in particular Peak finding can be helpful to identify local maxima in the images.

Transition to one-dimensional signals

When transitioning from a streak image to a one-dimensional signal, LumiSpy uses the axes units of the new signal (whether it is time units or not) to assign the correct 1D signal class: Either LumiSpectrum or LumiTransient. Such a reduction of the signal dimensionality can be done for example by slicing the signal with numpy-style indexing or by using functions such as sum() and integrate1D()

In the following image, the spectrum summed over all times is obtained from the streak image by:

>>> import lumispy as lum
>>> import numpy as np
...
>>> data = np.arange(10*20).reshape(10, 20)
>>> ax0 = {"name": "Time", "units": "ps", "size": 10}
>>> ax1 = {"name": "Wavelength", "units": "nm", "size": 20}
>>> s = lum.signals.LumiTransientSpectrum(data, axes = [ax0, ax1])
...
>>> s.sum(axis='Time')
>>> # Or alternatively:
>>> s.sum(axis=-1)
<LumiSpectrum, title: , dimensions: (|20)>

Similarly, the transient summed over all wavelengths is obtained by:

>>> s.sum(axis='Wavelength')
>>> # Or alternatively:
>>> s.sum(axis=-2)
<LumiTransient, title: , dimensions: (|10)>

The following image shows the plot of a streak camera image and its one-dimensional representations obtained by summing over either the wavelength or time dimensions:

Iteration over the spectral or time dimension

When performing an analysis where the same methods (e.g. a model fit) should be applied at every spectral position or at every time, it is convenient and more efficient to iterate over the corresponding axis. Therefore, this axis should be converted from a signal to a navigation dimension. To this end, the LumiTransientSpectrum class provides the functions spec2nav() and time2nav() to convert streak images to the LumiTransient and LumiSpectrum classes, respectively. Both functions return a new signal. By default (optimize=True), the methods ensure that the data is stored optimally, hence often making a copy of the data.

>>> import lumispy as lum
>>> import numpy as np
...
>>> data = np.ones(100).reshape(10, 10)
>>> ax0 = {"name": "Time", "units": "ps", "size": 10}
>>> ax1 = {"name": "Wavelength", "units": "nm", "size": 10}
>>> s = lum.signals.LumiTransientSpectrum(data, axes = [ax0, ax1])
...
>>> s
<LumiTransientSpectrum, title: , dimensions: (|10, 10)>
>>> s.spec2nav()
<LumiTransient, title: , dimensions: (10|10)>
>>> s.time2nav()
<LumiSpectrum, title: , dimensions: (10|10)>

Note

In case of existing navigation axes (e.g. linescan or map of streak images), the new axis is added as first axis:

>>> data = np.ones(1000).reshape(10, 10, 10)
>>> ax0 = {"name": "Position", "units": "px", "size": 10}
>>> ax1 = {"name": "Time", "units": "ps", "size": 10}
>>> ax2 = {"name": "Wavelength", "units": "nm", "size": 10}
>>> s = lum.signals.LumiTransientSpectrum(data, axes= [ax0, ax1, ax2])
...
>>> s.axes_manager[0]
<Position axis, size: 10, index: 0>
>>> s.time2nav().axes_manager[0]
<Time axis, size: 10, index: 0>