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    "info": {
        "author": "V. Armando Sol\u00e9",
        "author_email": "sole@esrf.fr",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: C++",
            "Programming Language :: Cython",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "====\nfisx\n====\n\nMain development website: https://github.com/vasole/fisx\n\n.. image:: https://travis-ci.org/vasole/fisx.svg?branch=master\n    :target: https://travis-ci.org/vasole/fisx\n\n.. image:: https://ci.appveyor.com/api/projects/status/github/vasole/fisx?branch=master&svg=true\n    :target: https://ci.appveyor.com/project/vasole/fisx\n\nThis 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.\n\nAccount for secondary excitation is made via the reference:\n\nD.K.G. de Boer, X-Ray Spectrometry 19 (1990) 145-154\n\nwith the correction mentioned in:\n\nD.K.G. de Boer et al, X-Ray Spectrometry 22 (1993) 33-28\n\nTertiary excitation is accounted for via an appproximation.\n\nThe accuracy of the corrections has been tested against experimental data and Monte Carlo simulations.\n\nLicense\n-------\n\nThis code is relased under the MIT license as detailed in the LICENSE file.\n\nInstallation\n------------\n\nTo 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 one of the ``pip install .`` or the ``python setup.py install`` approaches. It is convenient (but not mandatory) to have cython >= 0.17 installed for it.\n\nTesting\n-------\n\nTo run the tests **after installation** run::\n\n    python -m fisx.tests.testAll\n\nExample\n-------\n\nThere is a `web application <http://fisxserver.esrf.fr>`_ using this library for calculating expected x-ray count rates.\n\nThis piece of Python code shows how the library can be used via its python binding.\n\n.. code-block:: python\n\n  from fisx import Elements\n  from fisx import Material\n  from fisx import Detector\n  from fisx import XRF\n\n  elementsInstance = Elements()\n  elementsInstance.initializeAsPyMca()\n  # After the slow initialization (to be made once), the rest is fairly fast.\n  xrf = XRF()\n  xrf.setBeam(16.0) # set incident beam as a single photon energy of 16 keV\n  xrf.setBeamFilters([[\"Al1\", 2.72, 0.11, 1.0]]) # Incident beam filters\n  # Steel composition of Schoonjans et al, 2012 used to generate table I\n  steel = {\"C\":  0.0445, \n           \"N\":  0.04,\n           \"Si\": 0.5093,\n           \"P\":  0.02,\n           \"S\":  0.0175,\n           \"V\":  0.05,\n           \"Cr\":18.37,\n           \"Mn\": 1.619,\n           \"Fe\":64.314, # calculated by subtracting the sum of all other elements\n           \"Co\": 0.109,\n           \"Ni\":12.35,\n           \"Cu\": 0.175,\n           \"As\": 0.010670,\n           \"Mo\": 2.26,\n           \"W\":  0.11,\n           \"Pb\": 0.001}\n  SRM_1155 = Material(\"SRM_1155\", 1.0, 1.0)\n  SRM_1155.setComposition(steel)\n  elementsInstance.addMaterial(SRM_1155)\n  xrf.setSample([[\"SRM_1155\", 1.0, 1.0]]) # Sample, density and thickness\n  xrf.setGeometry(45., 45.)               # Incident and fluorescent beam angles\n  detector = Detector(\"Si1\", 2.33, 0.035) # Detector Material, density, thickness\n  detector.setActiveArea(0.50)            # Area and distance in consistent units\n  detector.setDistance(2.1)               # expected cm2 and cm.\n  xrf.setDetector(detector)\n  Air = Material(\"Air\", 0.0012048, 1.0)\n  Air.setCompositionFromLists([\"C1\", \"N1\", \"O1\", \"Ar1\", \"Kr1\"],\n                              [0.0012048, 0.75527, 0.23178, 0.012827, 3.2e-06])\n  elementsInstance.addMaterial(Air)\n  xrf.setAttenuators([[\"Air\", 0.0012048, 5.0, 1.0],\n                      [\"Be1\", 1.848, 0.002, 1.0]]) # Attenuators\n  fluo = xrf.getMultilayerFluorescence([\"Cr K\", \"Fe K\", \"Ni K\"],\n                                       elementsInstance,\n                                       secondary=2,\n                                       useMassFractions=1)\n  print(\"Element   Peak          Energy       Rate      Secondary  Tertiary\")\n  for key in fluo:\n      for layer in fluo[key]:\n          peakList = list(fluo[key][layer].keys())\n          peakList.sort()\n          for peak in peakList:\n              # energy of the peak\n              energy = fluo[key][layer][peak][\"energy\"]\n              # expected measured rate\n              rate = fluo[key][layer][peak][\"rate\"]\n              # primary photons (no attenuation and no detector considered)\n              primary = fluo[key][layer][peak][\"primary\"]\n              # secondary photons (no attenuation and no detector considered)\n              secondary = fluo[key][layer][peak][\"secondary\"]\n              # tertiary photons (no attenuation and no detector considered)\n              tertiary = fluo[key][layer][peak].get(\"tertiary\", 0.0)\n              # correction due to secondary excitation\n              enhancement2 = (primary + secondary) / primary\n              enhancement3 = (primary + secondary + tertiary) / primary\n              print(\"%s   %s    %.4f     %.3g     %.5g    %.5g\" % \\\n                                 (key, peak + (13 - len(peak)) * \" \", energy,\n                                 rate, enhancement2, enhancement3))\n\n\n\n",
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