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Metadata-Version: 2.1
Name: fisx
Version: 1.3.2
Summary: Quantitative X-Ray Fluorescence Analysis Support Library
Home-page: https://github.com/vasole/fisx
Download-URL: https://github.com/vasole/fisx/archive/v1.3.2.tar.gz
Author: V. Armando Solé
Author-email: sole@esrf.fr
License: MIT
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Cython
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
License-File: LICENSE
Requires-Dist: numpy
====
fisx
====
Main development website: https://github.com/vasole/fisx
.. image:: https://travis-ci.org/vasole/fisx.svg?branch=master
:target: https://travis-ci.org/vasole/fisx
.. image:: https://ci.appveyor.com/api/projects/status/github/vasole/fisx?branch=master&svg=true
:target: https://ci.appveyor.com/project/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 <http://fisxserver.esrf.fr>`_ 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.
.. code-block:: python
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))
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