fisx 1.3.2

Quantitative X-Ray Fluorescence Analysis Support Library

Main development website: https://github.com/vasole/fisx

This software library implements formulas to calculate, given an experimental setup, the expected x-ray fluorescence intensities. The library accounts for secondary and tertiary excitation, K, L and M shell emission lines and de-excitation cascade effects. The basic implementation is written in C++ and a Python binding is provided.

Account for secondary excitation is made via the reference:

D.K.G. de Boer, X-Ray Spectrometry 19 (1990) 145-154

with the correction mentioned in:

D.K.G. de Boer et al, X-Ray Spectrometry 22 (1993) 33-28

Tertiary excitation is accounted for via an appproximation.

The accuracy of the corrections has been tested against experimental data and Monte Carlo simulations.

License

This code is relased under the MIT license as detailed in the LICENSE file.

Installation

To install the library for Python just use pip install fisx. If you want build the library for python use from the code source repository, just use the pip install . approach.

Testing

To run the tests after installation run:

python -m fisx.tests.testAll

Example

There is a web application using this library for calculating expected x-ray count rates.

This piece of Python code shows how the library can be used via its python binding.

from fisx import Elements
from fisx import Material
from fisx import Detector
from fisx import XRF

elementsInstance = Elements()
elementsInstance.initializeAsPyMca()
# After the slow initialization (to be made once), the rest is fairly fast.
xrf = XRF()
xrf.setBeam(16.0) # set incident beam as a single photon energy of 16 keV
xrf.setBeamFilters([["Al1", 2.72, 0.11, 1.0]]) # Incident beam filters
# Steel composition of Schoonjans et al, 2012 used to generate table I
steel = {"C":  0.0445,
         "N":  0.04,
         "Si": 0.5093,
         "P":  0.02,
         "S":  0.0175,
         "V":  0.05,
         "Cr":18.37,
         "Mn": 1.619,
         "Fe":64.314, # calculated by subtracting the sum of all other elements
         "Co": 0.109,
         "Ni":12.35,
         "Cu": 0.175,
         "As": 0.010670,
         "Mo": 2.26,
         "W":  0.11,
         "Pb": 0.001}
SRM_1155 = Material("SRM_1155", 1.0, 1.0)
SRM_1155.setComposition(steel)
elementsInstance.addMaterial(SRM_1155)
xrf.setSample([["SRM_1155", 1.0, 1.0]]) # Sample, density and thickness
xrf.setGeometry(45., 45.)               # Incident and fluorescent beam angles
detector = Detector("Si1", 2.33, 0.035) # Detector Material, density, thickness
detector.setActiveArea(0.50)            # Area and distance in consistent units
detector.setDistance(2.1)               # expected cm2 and cm.
xrf.setDetector(detector)
Air = Material("Air", 0.0012048, 1.0)
Air.setCompositionFromLists(["C1", "N1", "O1", "Ar1", "Kr1"],
                            [0.0012048, 0.75527, 0.23178, 0.012827, 3.2e-06])
elementsInstance.addMaterial(Air)
xrf.setAttenuators([["Air", 0.0012048, 5.0, 1.0],
                    ["Be1", 1.848, 0.002, 1.0]]) # Attenuators
fluo = xrf.getMultilayerFluorescence(["Cr K", "Fe K", "Ni K"],
                                     elementsInstance,
                                     secondary=2,
                                     useMassFractions=1)
print("Element   Peak          Energy       Rate      Secondary  Tertiary")
for key in fluo:
    for layer in fluo[key]:
        peakList = list(fluo[key][layer].keys())
        peakList.sort()
        for peak in peakList:
            # energy of the peak
            energy = fluo[key][layer][peak]["energy"]
            # expected measured rate
            rate = fluo[key][layer][peak]["rate"]
            # primary photons (no attenuation and no detector considered)
            primary = fluo[key][layer][peak]["primary"]
            # secondary photons (no attenuation and no detector considered)
            secondary = fluo[key][layer][peak]["secondary"]
            # tertiary photons (no attenuation and no detector considered)
            tertiary = fluo[key][layer][peak].get("tertiary", 0.0)
            # correction due to secondary excitation
            enhancement2 = (primary + secondary) / primary
            enhancement3 = (primary + secondary + tertiary) / primary
            print("%s   %s    %.4f     %.3g     %.5g    %.5g" % \
                               (key, peak + (13 - len(peak)) * " ", energy,
                               rate, enhancement2, enhancement3))