Analysis of simulated EDX data

In this notebook we showcase the analysis of the built-in simulated dataset. We use the espm EDXS modelling to simulate the data. We first perform a KL-NMF decomposition of the data and then plot the results.

Imports

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%load_ext autoreload
%autoreload 2
%matplotlib inline

# Generic imports
import hyperspy.api as hs
import numpy as np

# espm imports
from espm.estimators import SmoothNMF
import espm.datasets as ds

Generating artificial datasets and loading them

If the datasets were already generated, they are not generated again

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# Generate the data
# Here `seeds_range` is the number of different samples to generate
ds.generate_built_in_datasets(seeds_range=1)

# Load the data
spim = ds.load_particules(sample = 0)
# We need to change the data to floats for the decomposition to run
spim.change_dtype('float64')

Building G

The information in the metadata of the spim object are used to build the G matrix. This matrix contains a model of the characteristic X-rays and the bremsstrahlung.

[3]:

spim.build_G()

Problem solving

Picking analysis parameters

  • 3 components for 3 phases

  • convergence criterions : tol and max_iter. tol is the minimum change of the loss function between two iterations. max_iter is the max number of iterations.

  • G : The EDX model is integrated in the decomposition. The algorithm learns the concentration of each chemical element and the bremsstrahlung parameters.

  • hspy_comp is a required parameter if you want to use the hyperspy api

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est = SmoothNMF( n_components = 3, tol = 1e-6, max_iter = 200, G = spim.model,hspy_comp = True)

Calculating the decomposition

/! It should take a minute to execute in the case of the built-in dataset

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out = spim.decomposition(algorithm = est, return_info=True)

Getting the losses and the results of the decomposition

  • First cell : Printing the resulting concentrations.

  • Second cell : Ploting the resulting spectra

  • Thrid cell : Ploting the resulting abundances

Hyperspy is mainly designed to be used with the qt graphical backend of matplotlib. Thus two plots will appear if you are in inline mode.

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spim.print_concentration_report()

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spim.plot_decomposition_loadings(3)

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spim.plot_decomposition_factors(3)

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