def FitProcessSpectrum(self, pymca_config, spectrum):
advancedFit = ClassMcaTheory.McaTheory(config=pymca_config)
advancedFit.enableOptimizedLinearFit()
mfTool = ConcentrationsTool(pymca_config)
tconf = mfTool.configure()
advancedFit.config['fit']['use_limit'] = 1
advancedFit.setData(y=spectrum)
advancedFit.estimate()
fitresult, tmpresult = advancedFit.startfit(digest=1)
tmpresult = advancedFit.imagingDigestResult()
temp = {}
temp['fitresult'] = fitresult
temp['result'] = tmpresult
for bcts, pk in zip(self.BckndCounts, self.peaks_quant):
tmpresult[pk]['fitarea'] -= bcts
temp['result']['config'] = advancedFit.config
tconf.update(advancedFit.configure()['concentrations'])
conc = mfTool.processFitResult(config=tconf,
fitresult=temp,
elementsfrommatrix=False,
fluorates=advancedFit._fluoRates)
calc_mf = numpy.float32(
[conc['mass fraction'][pk] for pk in self.peaks_quant])
return calc_mf, tmpresult
def __init__(self):
self.mcafit = ClassMcaTheory.McaTheory()
self.ctool = ConcentrationsTool.ConcentrationsTool()
self.app = None
def PerformFit(
filelist,
cfgfile,
energies,
mlines={},
norm=None,
fast=False,
addhigh=0,
prog=None,
plot=False,
):
"""Fit XRF spectra in batch with changing primary beam energy.
Args:
filelist(list(str)|np.array): spectra to fit
cfgfile(str): configuration file to use
energies(np.array): primary beam energies
mlines(Optional(dict)): elements (keys) which M line group must be replaced by some M subgroups (values)
norm(Optional(np.array)): normalization array
fast(Optional(bool)): fast fitting (linear)
addhigh(Optional(number)): add higher energy
prog(Optional(timing.ProgessLogger)): progress object
plot(Optional(bool))
Returns:
dict: {label:nenergies x nfiles,...}
"""
# Load data
# Each spectrum (each row) in 1 edf file is acquired at a different energy
if isinstance(filelist, list):
dataStack = EDFStack.EDFStack(filelist, dtype=np.float32).data
else:
dataStack = filelist
nfiles, nenergies, nchannels = dataStack.shape
# MCA channels
xmin = 0
xmax = nchannels - 1
x = np.arange(nchannels, dtype=np.float32)
# Energies
if hasattr(energies, "__iter__"):
if len(energies) == 1:
energies = [energies[0]] * nenergies
elif len(energies) != nenergies:
raise ValueError(
"Expected {} energies ({} given)".format(nenergies, len(energies))
)
else:
energies = [energies] * nenergies
# Normalization
if norm is None:
norm = [1] * nenergies
else:
if hasattr(norm, "__iter__"):
if len(norm) == 1:
norm = [norm[0]] * nenergies
elif len(norm) != nenergies:
raise ValueError(
"Expected {} normalization values ({} given)".format(
nenergies, len(norm)
)
)
else:
norm = [norm] * nenergies
# Prepare plot
if plot:
fig, ax = plt.subplots()
# Prepare fit
# ClassMcaTheory.DEBUG = 1
mcafit = ClassMcaTheory.McaTheory()
try:
mcafit.useFisxEscape(True)
except:
pass
if fast:
mcafit.enableOptimizedLinearFit()
else:
mcafit.disableOptimizedLinearFit()
cfg = mcafit.configure(ReadPyMcaConfigFile(cfgfile))
# Fit at each energy
if prog is not None:
prog.setnfine(nenergies * nfiles)
ret = {}
for j in range(nenergies):
# Prepare fit with this energy
AdaptPyMcaConfig(cfg, energies[j], mlines=mlines, fast=fast, addhigh=addhigh)
mcafit.configure(cfg)
# Fit all spectra with this energy
for i in range(nfiles):
# Data to fit
y = dataStack[i, j, :].flatten()
mcafit.setData(x, y, xmin=xmin, xmax=xmax)
# Initial parameter estimates
mcafit.estimate()
# Fit
fitresult = mcafit.startfit(digest=0)
# Extract result
if plot:
mcafitresult = mcafit.digestresult()
ax.cla()
if (
plot == 2
or not any(np.isfinite(np.log(mcafitresult["ydata"])))
or not any(mcafitresult["ydata"] > 0)
):
ax.plot(mcafitresult["energy"], mcafitresult["ydata"])
ax.plot(mcafitresult["energy"], mcafitresult["yfit"], color="red")
else:
ax.semilogy(mcafitresult["energy"], mcafitresult["ydata"])
ax.semilogy(
mcafitresult["energy"], mcafitresult["yfit"], color="red"
)
ax.set_ylim(
ymin=np.nanmin(
mcafitresult["ydata"][np.nonzero(mcafitresult["ydata"])]
)
)
ax.set_title("Primary energy: {} keV".format(energies[j]))
ax.set_xlabel("Energy (keV)")
ax.set_ylabel("Intensity (cts)")
plt.pause(0.0001)
else:
mcafitresult = mcafit.imagingDigestResult()
# Store result
for k in mcafitresult["groups"]:
if k not in ret:
ret[k] = np.zeros(
(nenergies, nfiles), dtype=type(mcafitresult[k]["fitarea"])
)
ret[k][j, i] = mcafitresult[k]["fitarea"] / norm[j]
if "chisq" not in ret:
ret["chisq"] = np.zeros((nenergies, nfiles), dtype=type(mcafit.chisq))
ret["chisq"][j, i] = mcafit.chisq
# Print progress
if prog is not None:
prog.ndonefine(nfiles)
prog.printprogress()
return ret