def mfit(data, xmin, xmax, ymin, ymax, dy):
fs = [Function('Linear', linear), Function('Gaussian', gaussian)]
x = np.linspace(xmin, xmax, 200)
ylo, yhi = ymin, ymin+dy
while yhi < ymax:
slab = data[ylo:yhi,xmin:xmax]
diff = slab.nxsignal.shape[0]
cut = slab.sum([0])
cut.plot()
fit = Fit(cut, fs, use_errors=True)
y = np.array(fit.y)
for f in fs:
f.guess_parameters(fit.x, y)
y = y - f.function_values(fit.x)
fit.fit_data()
NXdata(fit.get_model(x), x).oplot('-')
if raw_input('Keep? [y,N] ') == 'y':
data[ylo:yhi,xmin:xmax] = slab - fit.get_model(f=fs[1]) / diff
ylo = yhi
yhi = ylo + dy
return data
def mfit(data, xmin, xmax, ymin, ymax, dy):
fs = [Function('Linear', linear), Function('Gaussian', gaussian)]
x = np.linspace(xmin, xmax, 200)
ylo, yhi = ymin, ymin + dy
while yhi < ymax:
slab = data[ylo:yhi, xmin:xmax]
diff = slab.nxsignal.shape[0]
cut = slab.sum([0])
cut.plot()
fit = Fit(cut, fs, use_errors=True)
y = np.array(fit.y)
for f in fs:
f.guess_parameters(fit.x, y)
y = y - f.function_values(fit.x)
fit.fit_data()
NXdata(fit.get_model(x), x).oplot('-')
if raw_input('Keep? [y,N] ') == 'y':
data[ylo:yhi, xmin:xmax] = slab - fit.get_model(f=fs[1]) / diff
ylo = yhi
yhi = ylo + dy
return data
def get_model(self, f=None):
self.read_parameters()
fit = Fit(self.data, self.functions)
if self.plot_checkbox.isChecked():
x = fit.x
else:
x = np.linspace(float(self.plot_minbox.text()),
float(self.plot_maxbox.text()), 1001)
return NXdata(NXfield(fit.get_model(x, f), name='model'),
NXfield(x, name=fit.data.nxaxes[0].nxname),
title='Fit Results')
def get_model(self, f=None):
self.read_parameters()
fit = Fit(self.data, self.functions)
if self.plot_checkbox.isChecked():
x = fit.x
else:
x = np.linspace(float(self.plot_minbox.text()),
float(self.plot_maxbox.text()), 1001)
return NXdata(NXfield(fit.get_model(x,f), name='model'),
NXfield(x, name=fit.data.nxaxes[0].nxname),
title = 'Fit Results')
