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.power_law_perc_area(E1, E2, r)
hyperspy.misc.eels.tools.ratio(edge_A, edge_B)
hyperspy.misc.eels.tools.rel_std_of_fraction(a, std_a, b, std_b, corr_factor=1)