A Simple Example using NeXLMatrixCorrection

Load the necessary libraries

using NeXLMatrixCorrection  # Inplements the algorithms (auto-loads NeXLCore)
using DataFrames            # For the table

Convert k-ratios to composition.

Define the measurement conditions

lbl = label("K458")  # This labels the measurement
# Define the measurement properties (:BeamEnery and :TakeOffAngle are required by most matrix correction algorithms)
unkProps = Dict(:BeamEnergy=>15.0e3, :TakeOffAngle=>deg2rad(40.0), :Coating=>Film(pure(n"C"), 7.0e-7))
stdProps = Dict(:BeamEnergy=>15.0e3, :TakeOffAngle=>deg2rad(40.0), :Coating=>Film(pure(n"C"), 15.0e-7))
# Create a list of the measurement k-ratios.
krs = [
    KRatio([n"O K-L3"], unkProps, stdProps, mat"SiO2", uv(0.746227,0.0010) ),
    KRatio([n"Si K-L3"], unkProps, stdProps, mat"SiO2", uv(0.441263,0.0012) ),
    KRatio([n"Zn K-L3"], unkProps, stdProps, mat"Zn", uv(0.027776,0.0002) ),
    KRatio([n"Ba L3-M5"], unkProps, stdProps, mat"BaCl", uv(0.447794,0.0020) )
]

4-element Array{KRatio,1}:
 k[SiO2, O K-L3] = 0.74623 ± 0.001
 k[SiO2, Si K-L3] = 0.44126 ± 0.0012
 k[Zn, Zn K-L3] = 0.027776 ± 0.0002
 k[BaCl, Ba L3-M5] = 0.44779 ± 0.002

Perform the Iteration

# Now perform the iteration on the k-ratios
res = quantify(lbl, krs)
# Tabulate the results...
asa(DataFrame, res, withZAF=true)

4×14 DataFrame. Omitted printing of 1 columns
│ Row │ Label │ Element │ Standard │ Lines    │ Mass Frac. │ Δ[Mass Frac.] 
│ k[Meas]  │ Δk[Meas] │ Generation │ Z        │ A        │ F        │ Coati
ng  │
│     │ Label │ String  │ String   │ String   │ Float64    │ Float64       
│ Float64? │ Float64? │ Float64?   │ Float64? │ Float64? │ Float64? │ Float
64? │
├─────┼───────┼─────────┼──────────┼──────────┼────────────┼───────────────
┼──────────┼──────────┼────────────┼──────────┼──────────┼──────────┼──────
────┤
│ 1   │ K458  │ Si      │ SiO2     │ Si K-L3  │ 0.23133    │ 0.00063       
│ 0.441263 │ 0.0012   │ 1.0        │ 1.16228  │ 0.767812 │ 1.00007  │ 0.999
057 │
│ 2   │ K458  │ Ba      │ BaCl     │ Ba L3-M5 │ 0.4218     │ 0.0019        
│ 0.447794 │ 0.002    │ 1.0        │ 0.816548 │ 1.03873  │ 0.994896 │ 0.999
944 │
│ 3   │ K458  │ O       │ SiO2     │ O K-L3   │ 0.32701    │ 0.00044       
│ 0.746227 │ 0.001    │ 1.0        │ 1.15781  │ 1.07826  │ 0.999621 │ 0.973
821 │
│ 4   │ K458  │ Zn      │ Zn       │ Zn K-L3  │ 0.030727   │ 0.00022       
│ 0.027776 │ 0.0002   │ 1.0        │ 0.919149 │ 0.983471 │ 1.0      │ 0.999
993 │

Now take a slightly different perspective that focuses more on iteration related data.

asa(DataFrame, res, withZAF=false)

4×8 DataFrame
│ Row │ Label │ Element │ Converged │ Iterations │ Mass Frac. │ Δ[Mass Frac
.] │ k[Meas]  │ k[Comp]   │
│     │ Label │ String  │ Bool      │ Int64      │ Float64    │ Float64    
   │ Float64? │ Float64?  │
├─────┼───────┼─────────┼───────────┼────────────┼────────────┼────────────
───┼──────────┼───────────┤
│ 1   │ K458  │ Si      │ 1         │ 7          │ 0.23133    │ 0.00063    
   │ 0.441263 │ 0.441263  │
│ 2   │ K458  │ Ba      │ 1         │ 7          │ 0.4218     │ 0.0019     
   │ 0.447794 │ 0.447791  │
│ 3   │ K458  │ O       │ 1         │ 7          │ 0.32701    │ 0.00044    
   │ 0.746227 │ 0.746226  │
│ 4   │ K458  │ Zn      │ 1         │ 7          │ 0.030727   │ 0.00022    
   │ 0.027776 │ 0.0277758 │