def fit_curve():
global Plot1
ds = Plot1.ds
if len(ds) == 0:
print 'Error: no curve to fit in Plot1.'
return
for d in ds:
if d.title == 'fitting':
Plot1.remove_dataset(d)
d0 = ds[0]
fitting = Fitting(GAUSSIAN_FITTING)
try:
fitting.set_histogram(d0)
res = fitting.fit()
res.var[:] = 0
res.title = 'fitting'
Plot1.add_dataset(res)
mean = fitting.params['mean']
mean_err = fitting.error['mean']
print 'POS_OF_PEAK=' + str(mean) + '+/-' + str(mean_err)
print 'FWHM=' + str(2.35482 * math.fabs(fitting.params['sigma'])) \
+ '+/-' + str(5.54518 * fitting.error['sigma'])
print 'Chi2=' + str(fitting.fitter.getQuality())
peak_pos.value = fitting.mean
except:
print 'can not fit'
def plot2_fit_curve():
global Plot2
ds = Plot2.ds
if len(ds) == 0:
log('Error: no curve to fit in Plot2.\n')
return
for d in ds:
if d.title == 'fitting':
Plot2.remove_dataset(d)
if len(ds) == 1:
sds = ds[0]
else:
sds = Plot2.get_selected_dataset()
if sds is None:
open_error(
'Please select a curve to fit. Right click on the plot to focus on a curve. Or use CTRL + Mouse Click on a curve to select one.'
)
return
fitting = Fitting(GAUSSIAN_FITTING)
try:
fitting.set_histogram(sds, plot2_fit_min.value, plot2_fit_max.value)
val = plot2_peak_pos.value
if val == val:
fitting.set_param('mean', val)
val = plot2_FWHM.value
if val == val:
fitting.set_param('sigma', math.fabs(val / 2.35482))
res = fitting.fit()
res.var[:] = 0
res.title = 'fitting'
Plot2.add_dataset(res)
Plot2.pv.getPlot().setCurveMarkerVisible(Plot2.__get_NXseries__(res),
False)
mean = fitting.params['mean']
log('POS_OF_PEAK=' + str(mean))
log('FWHM=' + str(2.35482 * math.fabs(fitting.params['sigma'])))
log('Chi2 = ' + str(fitting.fitter.getQuality()))
plot2_peak_pos.value = fitting.mean
plot2_FWHM.value = 2.35482 * math.fabs(fitting.params['sigma'])
# print fitting.params
except:
# traceback.print_exc(file = sys.stdout)
log('can not fit\n')
def fit_curve():
global Plot1
ds = Plot1.ds
if len(ds) == 0:
log('Error: no curve to fit in Plot1.\n')
return
for d in ds:
if d.title == 'fitting':
Plot1.remove_dataset(d)
d0 = ds[0]
fitting = Fitting(GAUSSIAN_FITTING)
try:
fitting.set_histogram(d0, fit_min.value, fit_max.value)
val = peak_pos.value
if val == val:
fitting.set_param('mean', val)
val = FWHM.value
if val == val:
fitting.set_param('sigma', math.fabs(val / 2.35482))
res = fitting.fit()
res.var[:] = 0
res.title = 'fitting'
Plot1.add_dataset(res)
mean = fitting.params['mean']
mean_err = fitting.errors['mean']
FWHM.value = 2.35482 * math.fabs(fitting.params['sigma'])
FWHM_err = 5.54518 * math.fabs(fitting.errors['sigma'])
log('POS_OF_PEAK=' + str(mean) + ' +/- ' + str(mean_err))
log('FWHM=' + str(FWHM.value) + ' +/- ' + str(FWHM_err))
log('Chi2 = ' + str(fitting.fitter.getQuality()))
peak_pos.value = fitting.mean
# print fitting.params
except:
# traceback.print_exc(file = sys.stdout)
log('can not fit\n')