MatrixCorrection

| Documentation | |:----------------------------------------:| | |

Installation

Install NeXLMatrixCorrection using the Julia package manager

> ]add NeXLMatrixCorrection

or

> using Pkg
> Pkg.add("NeXLMatrixCorrection")

The matrix correction package in the NeXL microanalysis library for Julia. NeXLMatrixCorrection depends upon NeXLUncertainties and NeXLCore.

Quick Doc

Currently NeXLMatrixCorrection implements the XPP, CitZAF, XPhi and Riveros matrix correction and Reed fluorescence correction algorithms for bulk and coated samples. The library is designed to make it easy to add additional algorithms.

Primarily the algorithms in NeXLMatrixCorrection are designed to take a Vector{NeXLCore.KRatio} and return a NeXLCore.Material. Using broadcast syntax, it is possible to apply these algorithms directly to hyperspectrum data sets. However, there are versions optimized for hyper-spectral data sets, that take a Vector{KRatios} and return a NeXLCore.Materials. When used with (NeXLSpectrum)[https://github.com/usnistgov/NeXLSpectrum.jl], quantify(...) is extended to chain the fit_spectrum(...) operation to both fit standards to extract k-ratios and matrix correct both Spectrum and HyperSpectrum objects.

The k-ratio is a comparison of a measured intensity with the intensity from a reference (standard) material measured under the same conditions. Since they are intended for both WDS and EDS, the k-ratio can represent one or more characteristic X-ray lines from a single element. You can mix and match EDS and WDS k-ratios within a single matrix correction. You can also mix k-ratios measured at different beam energies or, hypothetically, even different instruments.

The primary methods are ```julia quantify( # Generic method for EDS, WDS or mixed data sample::Union{String, Label}, # Sample name or Label measured::Vector{KRatio}; # The k-ratios mc::Type{<:MatrixCorrection}=XPP, # Default algorithm choices fc::Type{<:FluorescenceCorrection}=ReedFluorescence, cc::Type{<:CoatingCorrection}=Coating)::IterationResult quantify(ffr::FilterFitResult)::IterationResult # Specialized for the results from fitted EDS spectra

where

KRatio( lines::AbstractVector{CharXRay}, # CharXRay or X-rays measured unkProps::Dict{Symbol,<:Any}, # Properties of the measurement ( :BeamEnery, :TakeOffAngle ) stdProps::Dict{Symbol,<:Any}, # Properties of the standard ( :BeamEnery, :TakeOffAngle ) standard::Material, # Composition of the standard kratio::AbstractFloat, # The k-ratio (can be an UncertainValue) ) ```

An example

```julia lbl = label("K458") unkProps = Dict(:BeamEnergy=>15.0e3, :TakeOffAngle=>deg2rad(40.0)) stdProps = unkProps # Same for both (in this case...) krs = [ KRatio([n"O K-L3"], unkProps, stdProps, mat"SiO2", 0.746227 ), KRatio([n"Si K-L3"], unkProps, stdProps, mat"SiO2", 0.441263 ), KRatio([n"Zn K-L3"], unkProps, stdProps, mat"Zn", 0.027776 ), KRatio([n"Ba L3-M5"], unkProps, stdProps, mat"BaCl", 0.447794 ) ] res = quantify(lbl, krs)

Converged to K458[Si=0.2311,Ba=0.4212,O=0.3192,Zn=0.0307] in 7 steps

```

An example using NeXLSpectrum

julia using NeXLSpectrum path=joinpath(datadir(), "exp_raw", "ADM6005a spectra") refs=references( [ reference(n"C", joinpath(path, "C std.msa"), mat"C"), reference(n"O", joinpath(path, "SiO2 std.msa"), mat"SiO2"), reference(n"Si", joinpath(path, "SiO2 std.msa"), mat"SiO2"), reference(n"Al", joinpath(path, "Al std.msa"), mat"Al"), reference(n"Ca", joinpath(path, "CaF2 std.msa"), mat"CaF2"), reference(n"Ti", joinpath(path, "Ti trimmed.msa"), mat"Ti"), reference(n"Zn", joinpath(path, "Zn std.msa"), mat"Zn"), reference(n"Ge", joinpath(path, "Ge std.msa"), mat"Ge"), ], 132.0 ) spec = sp = loadspectrum(joinpath(datadir(), "exp_raw", "ADM6005a spectra", "ADM-6005a_1.msa")) quantify(spec, refs, strip = [ n"C" ], coating = n"C K-L2"=>parse(Material, "C", density=1.9))