hyperspy.misc.eels.tools module

hyperspy.misc.eels.tools.eels_constant(s, zlp, t)

Calculate the constant of proportionality (k) in the relationship between the EELS signal and the dielectric function. dielectric function from a single scattering distribution (SSD) using the Kramers-Kronig relations.

S(E)=\frac{I_{0}t}{\pi a_{0}m_{0}v^{2}}\ln\left[1+\left(\frac{\beta}
{\theta_{E}}\right)^{2}\right]\Im(\frac{-1}{\epsilon(E)})=
k\Im(\frac{-1}{\epsilon(E)})

Parameters
  • zlp ({number, BaseSignal}) – If the ZLP is the same for all spectra, the intengral of the ZLP can be provided as a number. Otherwise, if the ZLP intensity is not the same for all spectra, it can be provided as i) a Signal of the same dimensions as the current signal containing the ZLP spectra for each location ii) a Signal of signal dimension 0 and navigation_dimension equal to the current signal containing the integrated ZLP intensity.

  • t ({None, number, BaseSignal}) – The sample thickness in nm. If the thickness is the same for all spectra it can be given by a number. Otherwise, it can be provided as a Signal with signal dimension 0 and navigation_dimension equal to the current signal.

Returns

k

Return type

Signal instance

hyperspy.misc.eels.tools.estimate_variance_parameters(noisy_signal, clean_signal, mask=None, pol_order=1, higher_than=None, return_results=False, plot_results=True, weighted=False, store_results='ask')

Find the scale and offset of the Poissonian noise

By comparing an SI with its denoised version (i.e. by PCA), this plots an estimation of the variance as a function of the number of counts and fits a polynomy to the result.

Parameters
  • clean_SI (noisy_SI,) –

  • mask (numpy bool array) – To define the channels that will be used in the calculation.

  • pol_order (int) – The order of the polynomy.

  • higher_than (float) – To restrict the fit to counts over the given value.

  • return_results (Bool) –

  • plot_results (Bool) –

  • store_results ({True, False, "ask"}, default "ask") – If True, it stores the result in the signal metadata

Returns

  • Dictionary with the result of a linear fit to estimate the offset

  • and scale factor

hyperspy.misc.eels.tools.get_edges_near_energy(energy, width=10, only_major=False, order='closest')

Find edges near a given energy that are within the given energy window.

Parameters
  • energy (float) – Energy to search, in eV

  • width (float) – Width of window, in eV, around energy in which to find nearby energies, i.e. a value of 10 eV (the default) means to search +/- 5 eV. The default is 10.

  • only_major (bool) – Whether to show only the major edges. The default is False.

  • order (str) – Sort the edges, if ‘closest’, return in the order of energy difference, if ‘ascending’, return in ascending order, similarly for ‘descending’

Returns

edges – All edges that are within the given energy window, sorted by energy difference to the given energy.

Return type

list

hyperspy.misc.eels.tools.get_info_from_edges(edges)

Return the information of a sequence of edges as a list of dictionaries

Parameters

edges (str or iterable) – the sequence of edges, each entry in the format of ‘element_subshell’.

Returns

info – a list of dictionaries with information corresponding to the provided edges.

Return type

list