from typing import Dict, NamedTuple, Sequence, Tuple, Union
import numpy
from PyMca5.PyMcaPhysics.xrf.ClassMcaTheory import McaTheory
from PyMca5.PyMcaIO.ConfigDict import ConfigDict
[docs]class EmissionLineGroup(NamedTuple):
element: str
name: str
counts: Union[float, Sequence]
@property
def counts_asarray(self):
return get_counts_array(self.counts)
[docs]class ScatterLineGroup(NamedTuple):
name: str
counts: Union[float, Sequence]
prefix: str = "Scatter"
@property
def counts_asarray(self):
return get_counts_array(self.counts)
[docs]def get_counts_array(counts: Union[float, Sequence]):
if isinstance(counts, Sequence):
return numpy.asarray(counts)
return numpy.array((counts))
[docs]def xrf_spectra(
linegroups: Sequence[EmissionLineGroup],
scattergroups: Sequence[ScatterLineGroup],
nchannels: int = 1024,
energy: float = 7.5,
angle_in: float = 62.0,
angle_out: float = 49.0,
flux: float = 1e10,
elapsed_time: float = 1.0,
) -> Tuple[numpy.ndarray, ConfigDict]:
"""The returned spectra have the shape `counts.shape + (nchannels,)`"""
peaks = {linegroup.element: linegroup.name for linegroup in linegroups}
counts: Dict[str, numpy.ndarray] = {
f"{group.element} {group.name}": group.counts_asarray for group in linegroups
}
counts.update(
{f"Scatter {group.name}": group.counts_asarray for group in scattergroups}
)
# MCA parameters
zero = 0 # keV
max_energy = energy + 1.5 # keV
gain = (max_energy - zero) / nchannels
# Initialize theory
theory = McaTheory()
theory.setConfiguration(
get_configuration(
peaks,
xmin=1,
xmax=nchannels - 3,
zero=zero,
gain=gain,
energy=energy,
angle_in=angle_in,
angle_out=angle_out,
flux=flux,
elapsed_time=elapsed_time,
)
)
# Calculate emission lines
x = numpy.arange(nchannels)
theory.setData(x=x, y=numpy.zeros(nchannels))
theory.enableOptimizedLinearFit()
theory.estimate()
# Analytical XRF spectrum
nglobal = theory.NGLOBAL
if counts:
shape = next(iter(counts.values())).shape
else:
shape = tuple()
y = numpy.zeros(shape + (nchannels,))
for i, name in enumerate(theory.PARAMETERS[nglobal:], nglobal):
normalized_group = theory.linearMcaTheoryDerivative(
numpy.asarray(theory.parameters), i, x
)
assert isinstance(normalized_group, numpy.ndarray)
if shape:
y += counts[name][..., None] * normalized_group[None, ...]
else:
y += counts[name] * normalized_group
return y, theory.config
[docs]def get_configuration(
peaks: dict,
xmin: int = 0,
xmax: int = 0,
zero: float = 0,
gain: float = 0.005,
energy: float = 0,
angle_in: float = 62.0,
angle_out: float = 49.0,
flux: float = 1e10,
elapsed_time: float = 1.0,
):
return {
"peaks": peaks,
"attenuators": get_attenuators(angle_in=angle_in, angle_out=angle_out),
"fit": get_fit_config(xmin=xmin, xmax=xmax, energy=energy),
"concentrations": get_quantification_config(
flux=flux, elapsed_time=elapsed_time
),
"detector": get_detector(zero=zero, gain=gain),
"peakshape": get_peak_shape(),
"materials": {
"SingleLayerStrategyMaterial": {
"Comment": "Last Single Layer Strategy iteration",
"Thickness": 0.0025,
"Density": 1.42,
"CompoundFraction": "",
"CompoundList": "",
},
**get_materials(),
},
"multilayer": {
"Layer0": [1, "Cerussite", 6.8, 0.1],
"Layer1": [0, "-", 0.0, 0.0],
"Layer2": [0, "-", 0.0, 0.0],
"Layer3": [0, "-", 0.0, 0.0],
"Layer4": [0, "-", 0.0, 0.0],
"Layer5": [0, "-", 0.0, 0.0],
"Layer6": [0, "-", 0.0, 0.0],
"Layer7": [0, "-", 0.0, 0.0],
"Layer8": [0, "-", 0.0, 0.0],
"Layer9": [0, "-", 0.0, 0.0],
},
"SingleLayerStrategy": {
"layer": "Auto",
"iterations": 3,
"completer": "-",
"peaks": "",
"materials": ["-", "-", "-", "-", "-", "-", "-", "-", "-", "-"],
"flags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
},
"tube": {
"transmission": 0,
"voltage": 30.0,
"anode": "Ag",
"anodethickness": 0.0002,
"anodedensity": 10.5,
"window": "Be",
"windowthickness": 0.0125,
"windowdensity": 1.848,
"filter1": "He",
"filter1thickness": 0.0,
"filter1density": 0.000118,
"alphax": 90.0,
"alphae": 90.0,
"deltaplotting": 0.1,
},
"fisx": {},
}
[docs]def get_quantification_config(flux: float = 1e10, elapsed_time: float = 1.0) -> dict:
return {
"usematrix": 0,
"useattenuators": 1,
"usemultilayersecondary": 0,
"usexrfmc": 0,
"mmolarflag": 0,
"flux": flux,
"time": elapsed_time,
"area": 0.774671,
"distance": 4.15114,
"reference": "Auto",
"useautotime": 0,
}
[docs]def get_peak_shape():
return {
"st_arearatio": 0.0697778,
"deltast_arearatio": 0.03,
"fixedst_arearatio": 1,
"st_sloperatio": 0.0105551,
"deltast_sloperatio": 0.49,
"fixedst_sloperatio": 1,
"lt_arearatio": 0.1,
"deltalt_arearatio": 0.15,
"fixedlt_arearatio": 1,
"lt_sloperatio": 0.1,
"deltalt_sloperatio": 0.08,
"fixedlt_sloperatio": 1,
"step_heightratio": 0.1,
"deltastep_heightratio": 5e-05,
"fixedstep_heightratio": 1,
"eta_factor": 0.02,
"deltaeta_factor": 0.02,
"fixedeta_factor": 1,
}
[docs]def get_fit_config(xmin: int = 0, xmax: int = 0, energy: float = 0) -> dict:
return {
"deltaonepeak": 0.01,
"strategy": "SingleLayerStrategy",
"strategyflag": 0,
"fitfunction": 0,
"continuum": 0,
"fitweight": 1,
"stripalgorithm": 1,
"linpolorder": 5,
"exppolorder": 6,
"stripconstant": 1.0,
"snipwidth": 65,
"stripiterations": 400,
"stripwidth": 5,
"stripfilterwidth": 7,
"stripanchorsflag": 0,
"maxiter": 10,
"deltachi": 0.001,
"xmin": xmin,
"xmax": xmax,
"linearfitflag": 1,
"use_limit": 0,
"stripflag": 1,
"escapeflag": 0,
"sumflag": 0,
"scatterflag": 1,
"hypermetflag": 1,
"stripanchorslist": [0, 0, 0, 0],
"energy": [energy],
"energyweight": [1.0],
"energyflag": [1],
"energyscatter": [1],
}
[docs]def get_attenuators(angle_in: float = 62.0, angle_out: float = 49.0) -> dict:
return {
"kapton": [1, "Mylar", 1.4, 0.0004, 1.0],
"atmosphere": [0, "-", 0.0, 0.0, 1.0],
"deadlayer": [0, "Si1", 2.33, 0.002, 1.0],
"absorber": [0, "-", 1.0, 0.1, 1.0],
"window": [1, "Be", 1.848, 0.0025, 1.0],
"contact": [0, "moxtekAP3.3", 5.45e-05, 1.0, 1.0],
"Filter 6": [0, "-", 0.0, 0.0, 1.0],
"Filter 7": [0, "-", 1.0, 0.1, 1.0],
"BeamFilter0": [0, "-", 0.0, 0.0, 1.0],
"BeamFilter1": [0, "-", 0.0, 0.0, 1.0],
"Detector": [1, "Si1", 2.33, 0.5, 1.0],
"Matrix": [
1,
"Cerussite",
6.8,
0.1,
angle_in,
angle_out,
0,
angle_in + angle_out,
],
}
[docs]def get_detector(zero: float = 0, gain: float = 0.005) -> dict:
return {
"detele": "Si",
"nthreshold": 4,
"zero": zero,
"deltazero": 0.1,
"fixedzero": 1,
"gain": gain,
"deltagain": 0.002,
"fixedgain": 1,
"noise": -0.0955892,
"deltanoise": 0.05,
"fixednoise": 1,
"fano": 0.0484433,
"deltafano": 0.114,
"fixedfano": 1,
"sum": 0.0,
"deltasum": 2e-06,
"fixedsum": 1,
"ignoreinputcalibration": 0,
}
[docs]def get_materials() -> dict:
return {
"Air": {
"Comment": "Dry Air (Near sea level) density=0.001204790 g/cm3",
"Thickness": 1.0,
"Density": 0.0012048,
"CompoundFraction": [0.000124, 0.75527, 0.23178, 0.012827, 3.2e-06],
"CompoundList": ["C1", "N1", "O1", "Ar1", "Kr1"],
},
"Goethite": {
"Comment": "Mineral FeO(OH) density from 3.3 to 4.3 density=4.3 g/cm3",
"CompoundFraction": 1.0,
"Thickness": 0.1,
"Density": 4.3,
"CompoundList": "Fe1O2H1",
},
"Mylar": {
"Comment": "Mylar (Polyethylene Terephthalate) density=1.40 g/cm3",
"Density": 1.4,
"CompoundFraction": [0.041959, 0.625017, 0.333025],
"CompoundList": ["H1", "C1", "O1"],
},
"Kapton": {
"Comment": "Kapton 100 HN 25 micron density=1.42 g/cm3",
"Thickness": 0.0025,
"Density": 1.42,
"CompoundFraction": [0.628772, 0.066659, 0.304569],
"CompoundList": ["C1", "N1", "O1"],
},
"Teflon": {
"Comment": "Teflon density=2.2 g/cm3",
"Density": 2.2,
"CompoundFraction": [0.240183, 0.759817],
"CompoundList": ["C1", "F1"],
},
"Viton": {
"Comment": "Viton Fluoroelastomer density=1.8 g/cm3",
"Density": 1.8,
"CompoundFraction": [0.009417, 0.280555, 0.710028],
"CompoundList": ["H1", "C1", "F1"],
},
"Water": {
"Comment": "Water density=1.0 g/cm3",
"CompoundFraction": 1.0,
"Thickness": 1.0,
"Density": 1.0,
"CompoundList": "H2O1",
},
"Gold": {
"Comment": "Gold",
"CompoundFraction": 1.0,
"Thickness": 1e-06,
"Density": 19.37,
"CompoundList": "Au",
},
"Cerussite": {
"Comment": "Mineral PbCO3",
"CompoundFraction": 1.0,
"Thickness": 0.1,
"Density": 6.8,
"CompoundList": "Pb1C1O3",
},
"mixture": {
"Comment": "New Material",
"Thickness": 1.0,
"Density": 1.0,
"CompoundFraction": [0.0096, 0.0234],
"CompoundList": ["Ca1S1O6H4", "K1Cl1"],
},
"sample": {
"Comment": "New Material",
"Thickness": 1.0,
"Density": 2.36,
"CompoundFraction": 1.0,
"CompoundList": "Hg1S1",
},
"moxtekAP3.3": {
"Comment": "New Material",
"Thickness": 1.0,
"Density": 5.45e-05,
"CompoundFraction": [0.06, 0.62, 0.055, 0.155, 0.106],
"CompoundList": ["B", "C", "N", "O", "Al"],
},
}
if __name__ == "__main__":
linegroups = [
EmissionLineGroup("Si", "K", 300),
EmissionLineGroup("Al", "K", 400),
EmissionLineGroup("Cl", "K", 200),
EmissionLineGroup("Pb", "M", 500),
EmissionLineGroup("P", "K", 200),
EmissionLineGroup("S", "K", 600),
EmissionLineGroup("Ca", "K", 500),
EmissionLineGroup("Ti", "K", 400),
EmissionLineGroup("Ce", "L", 500),
EmissionLineGroup("Fe", "K", 1000),
]
scattergroups = [
ScatterLineGroup("Peak000", 100),
ScatterLineGroup("Compton000", 100),
]
spectrum, config = xrf_spectra(linegroups, scattergroups)
import matplotlib.pyplot as plt
plt.plot(spectrum)
plt.show()