Mn L3/L2 white line ratio using gussian component plus H-S step edge

Mn_whitelineratio

%matplotlib widget
import hyperspy.api as hs
import matplotlib.pyplot as plt
import numpy as np
ll_sum = hs.load('ll_sum.hspy')
s_sum = hs.load('s_sum.hspy')

s_sum.metadata.Acquisition_instrument.TEM.beam_energy=200
s_sum.metadata.Acquisition_instrument.TEM.convergence_angle=22.5
s_sum.metadata.Acquisition_instrument.TEM.Detector.EELS.collection_angle=37.9
s = s_sum.isig[600.:700.]#.remove_background(signal_range=(600.,630.), fast=False)
ll = ll_sum

m = s.create_model(ll = ll, GOS="Hartree-Slater", auto_add_edges=False) # background is automatically added here from auto_background=True.
m.fit_component(m["PowerLaw"], bounded=True, signal_range=[610.,630.], fit_independent=True, only_current=True)
m.assign_current_values_to_all(components_list = [m["PowerLaw"]]) # This gives good (hopefully) initial values to all pixels

# Create and set the 4 components
L3 = hs.model.components1D.EELSCLEdge("Mn_L3", GOS="Hartree-Slater")
L2 = hs.model.components1D.EELSCLEdge("Mn_L2", GOS="Hartree-Slater")

L3_white = hs.model.components1D.Gaussian()
L3_white.name = "Mn_L3 line"
L3_white.centre.bmin = 640.0
L3_white.centre.bmax = 645.0
L3_white.centre.value = (L3_white.centre.bmax + L3_white.centre.bmin) / 2 # Initial value of the centre. Must be set to initialise the twinning below.
L3_white.A.bmin = 0
L3_white.sigma.bmax=3

L2_white = hs.model.components1D.Gaussian()
L2_white.name = "Mn_L2 line"
L2_white.centre.bmin = 650.0
L2_white.centre.bmax = 655.0
L2_white.centre.value = (L2_white.centre.bmax + L2_white.centre.bmin) / 2
L2_white.A.bmin = 0
L2_white.sigma.bmax=3

L3.onset_energy.twin = L3_white.centre
L2.onset_energy.twin = L2_white.centre

# Add all four to the model
m.extend([L3_white, L3, L2_white, L2])

# The following sortof emulates smart_fit, but it is more appropriate than smart_fit, 
# because smart_fit doesn't know that it should fit the gaussians between L3 and L2

# We fit "from the left", starting with the lowest energy
m.fit_component(L3_white,fitter="leastsq", signal_range=[L3_white.centre.bmin-3.0, L3_white.centre.bmax+3.0], bounded=True,) # get good values
m.assign_current_values_to_all(components_list = [L3_white]) # apply those good values to all pixels
m.fit_component(L3_white,fitter="leastsq", signal_range=[L3_white.centre.bmin-3.0, L3_white.centre.bmax+3.0], bounded=True, only_current=False)

m.fit_component(L3, bounded=True, signal_range=[636.,646.])
m.assign_current_values_to_all(components_list = [L3])
m.fit_component(L3, bounded=True, signal_range=[636.,646.], only_current=False)

m.fit_component(L2_white, signal_range=[L2_white.centre.bmin-3.0, L2_white.centre.bmax+3.0], bounded=True,)
m.assign_current_values_to_all(components_list = [L2_white])
m.fit_component(L2_white, signal_range=[L2_white.centre.bmin-3.0, L2_white.centre.bmax+3.0], bounded=True, only_current=False)

m.fit_component(L2, bounded=True, signal_range=[649., 659.])
m.assign_current_values_to_all(components_list = [L2])
m.fit_component(L2, bounded=True, signal_range=[649., 659.], only_current=False)

# Repeat this for each component
m.fit_component(L3, bounded=True, signal_range=[636.,646.], only_current=False)
m.fit_component(L3_white, signal_range=[L3_white.centre.bmin-3.0, L3_white.centre.bmax+3.0], bounded=True, only_current=False)
m.fit_component(L2, bounded=True, signal_range=[649., 659.], only_current=False)
m.fit_component(L2_white, signal_range=[L2_white.centre.bmin-3.0, L2_white.centre.bmax+3.0], bounded=True, only_current=False)

m.multifit(bounded=True) # optional final pass may improve fit by letting all components adjust to each other.