def save(result, outputDir, fileRoot=None, tif=False, csv=True):
from PyMca5.PyMca import ArraySave
if 'concentrations' in result:
imageNames = result['names']
images = numpy.concatenate(
(result['parameters'], result['concentrations']), axis=0)
else:
images = result['parameters']
imageNames = result['names']
nImages = images.shape[0]
if fileRoot in [None, ""]:
fileRoot = "images"
if not os.path.exists(outputDir):
os.mkdir(outputDir)
imagesDir = os.path.join(outputDir, "IMAGES")
if not os.path.exists(imagesDir):
os.mkdir(imagesDir)
imageList = [None] * (nImages + len(result['uncertainties']))
fileImageNames = [None] * (nImages + len(result['uncertainties']))
j = 0
for i in range(nImages):
name = imageNames[i].replace(" ", "-")
fileImageNames[j] = name
imageList[j] = images[i]
j += 1
if not imageNames[i].startswith("C("):
# fitted parameter
fileImageNames[j] = "s(%s)" % name
imageList[j] = result['uncertainties'][i]
j += 1
fileName = os.path.join(imagesDir, fileRoot + ".edf")
ArraySave.save2DArrayListAsEDF(imageList, fileName, labels=fileImageNames)
if csv:
ext = '.csv'
else:
ext = '.dat'
fileName = os.path.join(imagesDir, fileRoot + ext)
ArraySave.save2DArrayListAsASCII(imageList,
fileName,
csv=csv,
labels=fileImageNames)
if tif:
i = 0
for i in range(len(fileImageNames)):
label = fileImageNames[i]
if label.startswith("s("):
continue
elif label.startswith("C("):
mass_fraction = "_" + label[2:-1] + "_mass_fraction"
else:
mass_fraction = "_" + label
fileName = os.path.join(imagesDir,
fileRoot + mass_fraction + ".tif")
ArraySave.save2DArrayListAsMonochromaticTiff([imageList[i]],
fileName,
labels=[label],
dtype=numpy.float32)
def save(result, outputDir, fileRoot=None, tif=False, csv=True):
from PyMca5.PyMca import ArraySave
if 'concentrations' in result:
imageNames = result['names']
images = numpy.concatenate((result['parameters'],
result['concentrations']), axis=0)
else:
images = result['parameters']
imageNames = result['names']
nImages = images.shape[0]
if fileRoot in [None, ""]:
fileRoot = "images"
if not os.path.exists(outputDir):
os.mkdir(outputDir)
imagesDir = os.path.join(outputDir, "IMAGES")
if not os.path.exists(imagesDir):
os.mkdir(imagesDir)
imageList = [None] * (nImages + len(result['uncertainties']))
fileImageNames = [None] * (nImages + len(result['uncertainties']))
j = 0
for i in range(nImages):
name = imageNames[i].replace(" ","-")
fileImageNames[j] = name
imageList[j] = images[i]
j += 1
if not imageNames[i].startswith("C("):
# fitted parameter
fileImageNames[j] = "s(%s)" % name
imageList[j] = result['uncertainties'][i]
j += 1
fileName = os.path.join(imagesDir, fileRoot+".edf")
ArraySave.save2DArrayListAsEDF(imageList, fileName,
labels=fileImageNames)
if csv:
ext = '.csv'
else:
ext = '.dat'
fileName = os.path.join(imagesDir, fileRoot+ext)
ArraySave.save2DArrayListAsASCII(imageList, fileName, csv=csv,
labels=fileImageNames)
if tif:
i = 0
for i in range(len(fileImageNames)):
label = fileImageNames[i]
if label.startswith("s("):
continue
elif label.startswith("C("):
mass_fraction = "_" + label[2:-1] + "_mass_fraction"
else:
mass_fraction = "_" + label
fileName = os.path.join(imagesDir,
fileRoot + mass_fraction + ".tif")
ArraySave.save2DArrayListAsMonochromaticTiff([imageList[i]],
fileName,
labels=[label],
dtype=numpy.float32)
def _saveSingle(self):
from PyMca5.PyMca import ArraySave
imageNames = []
imageList = []
lst = [('parameter_names', 'parameters', '{}'),
('parameter_names', 'uncertainties', 's({})'),
('massfraction_names', 'massfractions', 'w({}){}')]
for names, key, fmt in lst:
images = self.get(key, None)
if images is not None:
for img in images:
imageList.append(img)
imageNames += self._getNames(names, fmt)
NexusUtils.mkdir(self.outroot_localfs)
if self.edf:
fileName = self.filename('.edf')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsEDF(imageList,
fileName,
labels=imageNames)
if self.csv:
fileName = self.filename('.csv')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsASCII(imageList,
fileName,
csv=True,
labels=imageNames)
if self.tif:
for label, image in zip(imageNames, imageList):
if label.startswith("s("):
suffix = "_s" + label[2:-1]
elif label.startswith("w("):
suffix = "_w" + label[2:-1]
else:
suffix = "_" + label
fileName = self.filename('.tif', suffix=suffix)
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsMonochromaticTiff(
[image], fileName, labels=[label], dtype=numpy.float32)
if self.cfg and 'configuration' in self:
fileName = self.filename('.cfg')
self._checkOverwrite(fileName)
self['configuration'].write(fileName)
def _saveSingle(self):
from PyMca5.PyMca import ArraySave
imageNames = []
imageList = []
lst = [('parameter_names', 'parameters', '{}'),
('parameter_names', 'uncertainties', 's({})'),
('massfraction_names', 'massfractions', 'w({}){}')]
for names, key, fmt in lst:
images = self.get(key, None)
if images is not None:
for img in images:
imageList.append(img)
imageNames += self._getNames(names, fmt)
NexusUtils.mkdir(self.outroot_localfs)
if self.edf:
fileName = self.filename('.edf')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsEDF(imageList, fileName,
labels=imageNames)
if self.csv:
fileName = self.filename('.csv')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsASCII(imageList, fileName, csv=True,
labels=imageNames)
if self.tif:
for label,image in zip(imageNames, imageList):
if label.startswith("s("):
suffix = "_s" + label[2:-1]
elif label.startswith("w("):
suffix = "_w" + label[2:-1]
else:
suffix = "_" + label
fileName = self.filename('.tif', suffix=suffix)
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsMonochromaticTiff([image],
fileName,
labels=[label],
dtype=numpy.float32)
if self.cfg and 'configuration' in self:
fileName = self.filename('.cfg')
self._checkOverwrite(fileName)
self['configuration'].write(fileName)
fileRoot = "images"
if not os.path.exists(outputDir):
os.mkdir(outputDir)
imagesDir = os.path.join(outputDir, "IMAGES")
if not os.path.exists(imagesDir):
os.mkdir(imagesDir)
imageList = [None] * (nImages)
fileImageNames = [None] * (nImages)
j = 0
for i in range(nImages):
name = imageNames[i].replace(" ", "-")
fileImageNames[j] = name
imageList[j] = images[i]
j += 1
fileName = os.path.join(imagesDir, fileRoot+".edf")
ArraySave.save2DArrayListAsEDF(imageList, fileName,
labels=fileImageNames)
fileName = os.path.join(imagesDir, fileRoot+".csv")
ArraySave.save2DArrayListAsASCII(imageList, fileName, csv=True,
labels=fileImageNames)
if tif:
i = 0
for i in range(len(fileImageNames)):
label = fileImageNames[i]
fileName = os.path.join(imagesDir,
fileRoot + fileImageNames[i] + ".tif")
ArraySave.save2DArrayListAsMonochromaticTiff([imageList[i]],
fileName,
labels=[label],
dtype=numpy.float32)
imageList = [None] * (nImages + len(result['uncertainties']))
fileImageNames = [None] * (nImages + len(result['uncertainties']))
j = 0
for i in range(nImages):
name = imageNames[i].replace(" ", "-")
fileImageNames[j] = name
imageList[j] = images[i]
j += 1
if not imageNames[i].startswith("C("):
# fitted parameter
fileImageNames[j] = "s(%s)" % name
imageList[j] = result['uncertainties'][i]
j += 1
fileName = os.path.join(imagesDir, fileRoot + ".edf")
ArraySave.save2DArrayListAsEDF(imageList,
fileName,
labels=fileImageNames)
fileName = os.path.join(imagesDir, fileRoot + ".csv")
ArraySave.save2DArrayListAsASCII(imageList,
fileName,
csv=True,
labels=fileImageNames)
if tif:
i = 0
for i in range(len(fileImageNames)):
label = fileImageNames[i]
if label.startswith("s("):
continue
elif label.startswith("C("):
mass_fraction = "_" + label[2:-1] + "_mass_fraction"
else:
def _saveImages(self):
from PyMca5.PyMca import ArraySave
# List of images in deterministic order
imageFileLabels, imageTitleLabels, imageList = self._imageList()
if not imageFileLabels:
return
NexusUtils.mkdir(self.outputDirLegacy)
if self.edf:
if self.multipage:
fileName = self.filename('.edf')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsEDF(imageList,
fileName,
labels=imageTitleLabels)
else:
for label, title, image in zip(imageFileLabels,
imageTitleLabels, imageList):
fileName = self.filename('.edf', suffix="_" + label)
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsEDF([image],
fileName,
labels=[title])
if self.tif:
if self.multipage:
fileName = self.filename('.tif')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsMonochromaticTiff(
imageList,
fileName,
labels=imageTitleLabels,
dtype=numpy.float32)
else:
for label, title, image in zip(imageFileLabels,
imageTitleLabels, imageList):
fileName = self.filename('.tif', suffix="_" + label)
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsMonochromaticTiff(
[image], fileName, labels=[title], dtype=numpy.float32)
if self.csv:
fileName = self.filename('.csv')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsASCII(imageList,
fileName,
csv=True,
labels=imageTitleLabels)
if self.dat:
fileName = self.filename('.dat')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsASCII(imageList,
fileName,
csv=False,
labels=imageTitleLabels)
if self.cfg and self._configurationkey in self:
fileName = self.filename('.cfg')
self._checkOverwrite(fileName)
self[self._configurationkey].write(fileName)
def _saveImages(self):
from PyMca5.PyMca import ArraySave
# List of images in deterministic order
imageFileLabels, imageTitleLabels, imageList = self._imageList()
if not imageFileLabels:
return
NexusUtils.mkdir(self.outroot_localfs)
if self.edf:
if self.multipage:
fileName = self.filename('.edf')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsEDF(imageList, fileName,
labels=imageTitleLabels)
else:
for label, title, image in zip(imageFileLabels, imageTitleLabels, imageList):
fileName = self.filename('.edf', suffix="_" + label)
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsEDF([image],
fileName,
labels=[title])
if self.tif:
if self.multipage:
fileName = self.filename('.tif')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsMonochromaticTiff(imageList,
fileName,
labels=imageTitleLabels,
dtype=numpy.float32)
else:
for label, title, image in zip(imageFileLabels, imageTitleLabels, imageList):
fileName = self.filename('.tif', suffix="_" + label)
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsMonochromaticTiff([image],
fileName,
labels=[title],
dtype=numpy.float32)
if self.csv:
fileName = self.filename('.csv')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsASCII(imageList, fileName, csv=True,
labels=imageTitleLabels)
if self.dat:
fileName = self.filename('.dat')
self._checkOverwrite(fileName)
ArraySave.save2DArrayListAsASCII(imageList, fileName, csv=False,
labels=imageTitleLabels)
if self.cfg and self._configurationkey in self:
fileName = self.filename('.cfg')
self._checkOverwrite(fileName)
self[self._configurationkey].write(fileName)
def processNotSynchronized(
specfile,
specnumbers,
destpath,
detectorcfg,
mlines={},
replacebasedir=None,
showelement=None,
dtcor=True,
fastfitting=True,
energyshift=0,
plot=False,
bkgxas=0,
bkgflux=0,
normmedian=False,
rois=None,
counters=None,
):
"""
XRF fitting of XANES spectra (fit repeats separately and add interpolated results because no energy synchronization)
Args:
specfile(str): name of the spec file
specnumbers(list(list(int))): list of lists of spec numbers
destpath(str): directory for saving the result
detectorcfg(list(str)): config files for fitting (one per detector)
replacebasedir(Optional(2-tuple)): replace first with second in the data directory extracted from the spec file
dtcor(Optional(True)): correct spectrum for deadtime before fitting
fastfitting(Optional(True)): linear fitting or non-linear
showelement(Optional(str)): element to be plotted
energyshift(Optional(num)): energy shift in keV
plot(Optional(bool)): plot results
mlines(Optional(dict)): elements (keys) which M line group must be replaced by some M subgroups (values)
bkgxas(Optional(Num)): subtract from XAS spectrum
bkgflux(Optional(Num)): subtract from iodet signal (cts/sec)
normmedian(Optional(bool)): normalize the XRF count normalization
rois(Optional(list(dict(2-tuple)))): ROIs instead of fitting
counters(Optional(dict)): list of counters to be treated as the XRF counts
"""
energylabel = "arr_energyM"
iodetlabel = "arr_iodet"
timelabel = "arr_mtime"
addbeforefit = (
True # refers to multiple detectors, repeats are always added afterwards
)
# Open spec file
sf = spec(specfile)
# Prepare
nxasspectra = len(specnumbers)
nrepeats = [len(l) for l in specnumbers]
nxasspectraT = sum(nrepeats)
if dtcor:
parsename = "dtcor"
else:
parsename = "copy"
logger = logging.getLogger(__name__)
prog = ProgressLogger(logger)
if not hasattr(detectorcfg, "__iter__"):
detectorcfg = [detectorcfg]
if isinstance(rois, dict):
rois = [rois]
if counters is None:
counters = {}
ncounters = len(counters)
counteroutnames = counters.keys()
counterinnames = [counters[c]["name"] for c in counters]
counterbkg = [counters[c]["bkg"] for c in counters]
counterminlog = [counters[c]["minlog"] for c in counters]
# Loop over spectra
off = 0
prog.start()
for i in range(nxasspectra):
# XAS spectrum: sum of all repeats and detectors
xasspectrum = {}
# Loop over repeats
nrepeats = len(specnumbers[i])
for j in range(nrepeats):
# Get energy and iodet
data, info = sf.getdata(specnumbers[i][j],
[energylabel, iodetlabel, timelabel] +
counterinnames)
realtime = sf.scancommand(specnumbers[i][j])["time"]
data[:, 0] += energyshift
energyj = data[:, 0][:, np.newaxis]
norm = (data[:, 1] / realtime - bkgflux) * data[:, 2]
if ncounters > 0:
ctrs = data[:, 2:]
for c in range(ncounters):
ctrs[:,
c] = (ctrs[:, c] / realtime - counterbkg[c]) * data[:,
2]
if normmedian:
norm /= np.median(norm)
# Parse xia files
datadir = info["DIRECTORY"]
if len(replacebasedir) == 2:
datadir = datadir.replace(replacebasedir[0], replacebasedir[1])
detectorcfg = [
f.replace(replacebasedir[0], replacebasedir[1])
for f in detectorcfg
]
scanname = info["RADIX"]
scannumber = int(info["ZAP SCAN NUMBER"])
if dtcor:
parsename = "dtcor"
else:
parsename = "copy"
parsename = (
"%%0%dd_%s" %
(np.int(np.floor(np.log10(nxasspectraT))) + 1, parsename) %
(off))
if j == 0:
destradix = scanname
outdir = os.path.join(destpath, destradix + "_data")
filestofit, detnums = parse_xia_esrf(
datadir,
scanname,
scannumber,
outdir,
parsename,
deadtime=dtcor,
add=addbeforefit,
)
ndets = len(filestofit)
# Intialize progress counter
if i == 0 and j == 0:
prog.setn(nxasspectraT * ndets)
# Fit, normalize and add spectra from all detector
xasspectrumj = {}
for k in range(ndets):
idet = detnums[k]
if len(filestofit[k]) != 0:
if rois is None:
if len(detectorcfg) == 1:
cfg = detectorcfg[0]
else:
cfg = detectorcfg[k]
# Perform fitting
xasresults = fitter(
filestofit[k],
cfg,
energyj,
mlines=mlines,
norm=norm,
fast=fastfitting,
prog=prog,
plot=plot,
)
else:
if len(rois) == 1:
roisk = rois[0]
else:
roisk = rois[k]
# Perform ROI summing
xasresults = roisummer(filestofit[k], roisk, norm=norm)
if len(xasspectrumj) == 0:
xasspectrumj = xasresults
elif energy_ref is None:
for group in xasresults:
xasspectrumj[group] += xasresults[group]
# Add normalized sum of counters
for c in range(ncounters):
tmp = ctrs[:, c]
if counterminlog[c]:
tmp = -np.log(tmp)
xasspectrumj[counteroutnames[c]] = tmp[:, np.newaxis]
# Add this repeat to the previous repeats (if any)
if len(xasspectrum) == 0:
xasspectrum = xasspectrumj
energy = energyj
else:
for group in xasspectrumj:
spl = InterpolatedUnivariateSpline(energyj,
xasspectrumj[group],
ext=0)
xasspectrum[group] += spl(energy)
# Show
if showelement in xasspectrum and plot:
pylab.clf()
pylab.plot(energy, xasspectrum[showelement][:, 0])
pylab.title("Spec #{}: {}/I0 (Summed repeats = {})".format(
specnumbers[i][j], showelement, j + 1))
pylab.pause(0.01)
# Show progress
prog.ndone(ndets)
prog.printprogress()
# What we want:
# XAS1 = sum(I)/sum(I0) = mu(fluo).rho.d
# What we have:
# XAS1 = sum(I/I0) = nrepeats.mu(fluo).rho.d
for group in xasspectrum:
xasspectrum[group] /= nrepeats
# Save XAS spectrum (for each element)
if specnumbers[i][0] == specnumbers[i][-1]:
outname = "{}_{:03d}".format(destradix, specnumbers[i][0])
else:
outname = "{}_{:03d}_{:03d}.sum".format(destradix,
specnumbers[i][0],
specnumbers[i][-1])
fileName = os.path.join(destpath, outname + ".dat")
if not os.path.exists(destpath):
os.makedirs(destpath)
xasspectrum["energyM"] = energy
labels = [k.replace(" ", "-") for k in xasspectrum]
ArraySave.save2DArrayListAsASCII(xasspectrum.values(),
fileName,
labels=labels)
logger.info("Saved XAS spectrum {}.".format(fileName))
def processEnergySynchronized(
specfile,
specnumbers,
destpath,
pymcacfg,
mlines={},
replacebasedir=(),
showelement=None,
dtcor=True,
fastfitting=True,
energyshift=0,
plot=False,
bkgxas=0,
bkgflux=0,
normmedian=False,
rois=None,
counters=None,
):
"""
XRF fitting of XANES spectra (add spectra from repeats because of energy synchronization)
Args:
specfile(str): name of the spec file
specnumbers(list(list(int))): list of lists of spec numbers
destpath(str): directory for saving the result
pymcacfg(str): config file for fitting
replacebasedir(Optional(2-tuple)): replace first with second in the data directory extracted from the spec file
dtcor(Optional(True)): correct spectrum for deadtime before fitting
fastfitting(Optional(True)): linear fitting or non-linear
showelement(Optional(str)): element to be plotted
energyshift(Optional(num)): energy shift in keV
plot(Optional(bool)): plot results
mlines(Optional(dict)): elements (keys) which M line group must be replaced by some M subgroups (values)
bkgxas(Optional(Num)): subtract from XAS spectrum
bkgflux(Optional(Num)): subtract from iodet signal (cts/sec)
normmedian(Optional(bool)): normalize the XRF count normalization
rois(Optional(dict(2-tuple))): ROIs instead of fitting
counters(Optional(dict)): list of counters to be treated as the XRF counts
"""
# /users/blissadm/spec/macros/zap/zapxmap.mac
# xmap_x1_00 gets its counts from (see ZAP_PSEUDO): _zap_xmap_roi_calc
# xmap_x1_00 = sum(XRF[a:b])/mtime*time
#
# /users/blissadm/spec/macros/zap/zaptools.mac
# arr_iodet = iodet/arr_mtime*realtime
#
# -> xanes = sum(XRF[a:b])/(mtime*arr_iodet)*time
energylabel = "arr_energyM"
iodetlabel = "arr_iodet"
timelabel = "arr_mtime"
# Open spec file
sf = spec(specfile)
# Prepare
nxasspectra = len(specnumbers)
nrepeats = [len(l) for l in specnumbers]
nxasspectraT = sum(nrepeats)
logger = logging.getLogger(__name__)
prog = ProgressLogger(logger)
if not os.path.exists(destpath):
os.makedirs(destpath)
if counters is None:
counters = {}
ncounters = len(counters)
counteroutnames = counters.keys()
counterinnames = [counters[c]["name"] for c in counters]
counterbkg = [counters[c]["bkg"] for c in counters]
# Loop over spectra
prog.setn(nxasspectra)
prog.start()
for i in range(nxasspectra):
# XAS spectrum: sum of all repeats and detectors
xasspectrum = {}
nrepeats = len(specnumbers[i])
xrfinfo = [{}] * nrepeats
# Get spec info
for j in range(nrepeats):
# Get energy and iodet
data, info = sf.getdata(specnumbers[i][j],
[energylabel, iodetlabel, timelabel] +
counterinnames)
realtime = sf.scancommand(specnumbers[i][j])["time"]
data[:, 0] += energyshift
# Check energy synchronization
if "energy" in xasspectrum:
if xasspectrum["nenergy"] != data.shape[0]:
raise ValueError(
"Number of energies in spec scan {} are not the same as for spec scan {}"
.format(specnumbers[i], specnumbers[0]))
if not np.allclose(
xasspectrum["energy"], data[:, 0], rtol=0, atol=1e-6):
raise ValueError(
"Energies in spec scan {} are not synchronized with energies in {}"
.format(specnumbers[i], specnumbers[0]))
else:
xasspectrum["nenergy"] = data.shape[0]
xasspectrum["energy"] = data[:, 0]
xasspectrum["norm"] = np.empty((data.shape[0], nrepeats),
dtype=data.dtype)
if ncounters != 0:
for c in counterinnames:
xasspectrum["counter_" + c] = np.empty(
(data.shape[0], nrepeats), dtype=data.dtype)
# data[:,1] = iodet/data[:,2]*realtime
# norm = (data[:,1]/realtime - bkgflux)*data[:,2]
# = iodet - bkgflux*data[:,2]
xasspectrum["norm"][:, j] = (data[:, 1] / realtime -
bkgflux) * data[:, 2]
xrfinfo[j] = info
if ncounters != 0:
for c in range(ncounters):
xasspectrum["counter_" + counterinnames[c]][:, j] = (
data[:, 3 + c] / realtime - counterbkg[c]) * data[:, 2]
if normmedian:
xasspectrum["norm"] /= np.median(xasspectrum["norm"])
# Generate normalized XRF spectra to be fitted
for j in range(nrepeats):
norm = xasspectrum["norm"][:, j][:, np.newaxis]
info = xrfinfo[j]
# Parse xia files
datadir = info["DIRECTORY"]
if len(replacebasedir) == 2:
datadir = datadir.replace(replacebasedir[0], replacebasedir[1])
if pymcacfg is not None:
pymcacfg = pymcacfg.replace(replacebasedir[0],
replacebasedir[1])
scanname = info["RADIX"]
scannumber = int(info["ZAP SCAN NUMBER"])
# Sum, dt correction and I0 normalize
fs = os.path.join(
datadir,
"%s_xia[0-9]*_%04d_0000_*.edf" % (scanname, scannumber))
detfiles = sorted(glob(fs))
if len(detfiles) == 0:
logger.warning("No files found with filter {}".format(fs))
fs = os.path.join(
datadir, "%s_xiast_%04d_0000_*.edf" % (scanname, scannumber))
stfile = glob(fs)
if len(stfile) == 0:
logger.warning("No files found with filter {}".format(fs))
if len(detfiles) == 0:
xia = None
if "data" not in xasspectrum:
xasspectrum["data"] = norm * 0
else:
xia = XiaEdf.XiaEdfScanFile(stfile[0], detfiles)
err = xia.sum(deadtime=dtcor)
if "data" in xasspectrum:
xasspectrum["data"] += xia.data / norm
else:
xasspectrum["data"] = xia.data / norm
# What we want:
# XAS = sum(I)/sum(I0) = mu(fluo).rho.d
# What we have:
# XAS = sum(I/I0) = nrepeats.mu(fluo).rho.d
xasspectrum["data"] /= nrepeats
# Subtract background
xasspectrum["data"] -= bkgxas
xasspectrum["data"][xasspectrum["data"] < 0] = 0
# Fit spectrum or take ROI
energy = xasspectrum["energy"]
if specnumbers[i][0] == specnumbers[i][-1]:
outname = "{}_{:03d}".format(scanname, specnumbers[i][0])
else:
outname = "{}_{:03d}_{:03d}.sum".format(scanname,
specnumbers[i][0],
specnumbers[i][-1])
if rois is None and xia is not None:
# Save XRF spectra to be fitted (not needed, just for checking the fit afterwards)
fileName = os.path.join(destpath, outname + ".edf")
xia.data = xasspectrum["data"]
xia.save(fileName, 1)
# Fit xas spectrum
datastack = xasspectrum["data"][np.newaxis, ...]
xasresults = fitter(
datastack,
pymcacfg,
energy,
mlines=mlines,
fast=fastfitting,
prog=prog,
plot=plot,
)
# Show fit result
if showelement in xasresults and plot:
pylab.clf()
pylab.plot(energy, xasresults[showelement][:, 0])
pylab.title("Spec #{}-#{}: {}/I0 ({} repeats)".format(
specnumbers[i][0], specnumbers[i][-1], showelement,
nrepeats))
pylab.pause(0.01)
else:
# datastack = xasspectrum["data"][np.newaxis,...]
# xasresults = roisummer(datastack,rois)
# More straightforward:
xasresults = {}
if rois is not None:
nen, nchan = xasspectrum["data"].shape
for label, roi in rois.items():
xasresults[label] = np.sum(
xasspectrum["data"][:, roi[0]:roi[1]], axis=1)[:, None]
# if True:
# import matplotlib.pyplot as plt
# plt.plot(xasresults["Fe-Ka"][:,0],label="script")
# plt.plot(data[:,-1],label="arr_absorp3")
# plt.title("{}: #{}".format(specfile,specnumbers[0][0]))
# plt.legend()
# plt.show()
# exit()
# Add energy to result
xasresults["energyM"] = energy[:, np.newaxis]
# Add normalized sum of counters
for c in counters:
tmp = np.sum(
xasspectrum["counter_" + counters[c]["name"]] /
xasspectrum["norm"],
axis=1,
)
tmp /= nrepeats
if counters[c]["minlog"]:
tmp = -np.log(tmp)
xasresults[c] = tmp[:, np.newaxis]
# Add norm
xasresults["norm"] = xasspectrum["norm"]
# Save XAS spectrum (for each element)
fileName = os.path.join(destpath, outname + ".dat")
labels = [k.replace(" ", "-") for k in xasresults]
ArraySave.save2DArrayListAsASCII(xasresults.values(),
fileName,
labels=labels)
logger.info("Saved XAS spectrum {}.".format(fileName))
# Show progress
prog.ndone(1)
prog.printprogress()