def onFitPeak(self, evt=None):
gname = self.groupname
dtext = []
model = self.fit_model.GetStringSelection().lower()
dtext.append('Fit Model: %s' % model)
bkg = self.fit_bkg.GetStringSelection()
if bkg == 'None':
bkg = None
if bkg is None:
dtext.append('No Background')
else:
dtext.append('Background: %s' % bkg)
step = self.fit_step.GetStringSelection().lower()
if model in ('step', 'rectangle'):
dtext.append('Step form: %s' % step)
try:
lgroup = getattr(self.larch.symtable, gname)
x = lgroup._xdat
y = lgroup._ydat
except AttributeError:
self.write_message('need data to fit!')
return
if step.startswith('error'):
step = 'erf'
elif step.startswith('arctan'):
step = 'atan'
pgroup = fit_peak(x,
y,
model,
background=bkg,
step=step,
_larch=self.larch)
dtext = '\n'.join(dtext)
dtext = '%s\n%s\n' % (
dtext, fit_report(
pgroup.params, min_correl=0.25, _larch=self.larch))
self.fit_report.SetEditable(True)
self.fit_report.SetValue(dtext)
self.fit_report.SetEditable(False)
lgroup.plot_yarrays = [(lgroup._ydat, PLOTOPTS_1, lgroup.plot_ylabel)]
if bkg is None:
lgroup._fit = pgroup.fit[:]
lgroup.plot_yarrays.append((lgroup._fit, PLOTOPTS_2, 'fit'))
else:
lgroup._fit = pgroup.fit[:]
lgroup._fit_bgr = pgroup.bkg[:]
lgroup.plot_yarrays.append((lgroup._fit, PLOTOPTS_2, 'fit'))
lgroup.plot_yarrays.append(
(lgroup._fit_bgr, PLOTOPTS_2, 'background'))
self.plot_group(gname, new=True)
def onFitPeak(self, evt=None):
gname = self.groupname
dtext = []
model = self.fit_model.GetStringSelection().lower()
dtext.append('Fit Model: %s' % model)
bkg = self.fit_bkg.GetStringSelection()
if bkg == 'None':
bkg = None
if bkg is None:
dtext.append('No Background')
else:
dtext.append('Background: %s' % bkg)
step = self.fit_step.GetStringSelection().lower()
if model in ('step', 'rectangle'):
dtext.append('Step form: %s' % step)
try:
lgroup = getattr(self.larch.symtable, gname)
x = lgroup._xdat_
y = lgroup._ydat_
except AttributeError:
self.write_message('need data to fit!')
return
if step.startswith('error'):
step = 'erf'
elif step.startswith('arctan'):
step = 'atan'
pgroup = fit_peak(x, y, model, background=bkg, step=step,
_larch=self.larch)
dtext = '\n'.join(dtext)
dtext = '%s\n%s\n' % (dtext, fit_report(pgroup.params, min_correl=0.25,
_larch=self.larch))
self.fit_report.SetEditable(True)
self.fit_report.SetValue(dtext)
self.fit_report.SetEditable(False)
popts1 = dict(style='solid', linewidth=3,
marker='None', markersize=4)
popts2 = dict(style='short dashed', linewidth=2,
marker='None', markersize=4)
lgroup.plot_yarrays = [(lgroup._ydat_, popts1, lgroup.plot_ylabel)]
if bkg is None:
lgroup._fit = pgroup.fit[:]
lgroup.plot_yarrays.append((lgroup._fit, popts2, 'fit'))
else:
lgroup._fit = pgroup.fit[:]
lgroup._fit_bgr = pgroup.bkg[:]
lgroup.plot_yarrays.append((lgroup._fit, popts2, 'fit'))
lgroup.plot_yarrays.append((lgroup._fit_bgr, popts2, 'background'))
self.onPlot()
def xrf_calib_fitrois(mca, _larch=None):
"""initial calibration step for MCA:
find energy locations for all ROIs
"""
if not isLarchMCAGroup(mca):
print('Not a valid MCA')
return
energy = 1.0 * mca.energy
chans = 1.0 * np.arange(len(energy))
counts = mca.counts
bgr = getattr(mca, 'bgr', None)
if bgr is not None:
counts = counts - bgr
calib = OrderedDict()
for roi in mca.rois:
words = roi.name.split()
elem = words[0].title()
family = 'ka'
if len(words) > 1:
family = words[1]
try:
eknown = xray_line(elem, family, _larch=_larch)[0] / 1000.0
except:
continue
llim = max(0, roi.left - roi.bgr_width)
hlim = min(len(chans) - 1, roi.right + roi.bgr_width)
fit = fit_peak(chans[llim:hlim],
counts[llim:hlim],
'Gaussian',
background='constant',
_larch=_larch)
ccen = fit.params.center.value
ecen = ccen * mca.slope + mca.offset
fwhm = 2.354820 * fit.params.sigma.value * mca.slope
calib[roi.name] = (eknown, ecen, fwhm, ccen, fit)
mca.init_calib = calib
def xrf_calib_fitrois(mca, _larch=None):
"""initial calibration step for MCA:
find energy locations for all ROIs
"""
if not isLarchMCAGroup(mca):
print( 'Not a valid MCA')
return
energy = 1.0*mca.energy
chans = 1.0*np.arange(len(energy))
counts = mca.counts
bgr = getattr(mca, 'bgr', None)
if bgr is not None:
counts = counts - bgr
calib = OrderedDict()
for roi in mca.rois:
words = roi.name.split()
elem = words[0].title()
family = 'ka'
if len(words) > 1:
family = words[1]
try:
eknown = xray_line(elem, family, _larch=_larch)[0]/1000.0
except:
continue
llim = max(0, roi.left - roi.bgr_width)
hlim = min(len(chans)-1, roi.right + roi.bgr_width)
fit = fit_peak(chans[llim:hlim], counts[llim:hlim],
'Gaussian', background='constant',
_larch=_larch)
ccen = fit.params['center'].value
ecen = ccen * mca.slope + mca.offset
fwhm = 2.354820 * fit.params['sigma'].value * mca.slope
calib[roi.name] = (eknown, ecen, fwhm, ccen, fit)
mca.init_calib = calib