def on_fit(self, e):
"""
Perform the fit, update the GUI, and plot the results.
:param e: Unused event
:return: None
"""
self.on_center(e)
self.psfun = self.ctlpsfun.GetSelection()
# Fit the results
fitout, fitdat = isolated_peak_fit(self.centdat[:, 0],
self.centdat[:, 1], self.psfun)
fitout = fitout[:, 0]
resolution = self.centdat[np.argmax(self.centdat[:, 1]), 0] / fitout[0]
error = np.sum(
(fitdat - self.centdat[:, 1]) * (fitdat - self.centdat[:, 1]))
# Update the GUI
self.ctlmzsig.SetValue(str(fitout[0]))
self.resbox.SetValue(str(resolution))
self.plot1.plotrefreshtop(self.centdat[:, 0],
self.centdat[:, 1],
title="Data",
xlabel="m/z (Th)",
ylabel="Normalized Intensity",
zoom="span")
self.plot1.plotadd(self.centdat[:, 0],
fitdat,
"blue",
"Peak Shape Guess",
nopaint=False)
self.errorbox.SetValue(str(error))
pass
def on_fit(self, e):
"""
Fits the spectrum and outputs the results.
:param e:
:return:
"""
self.on_center(e)
if self.flipflag == 0:
psfun = self.ctlpsfun.GetSelection()
else:
psfun = 0
# Fit the results
fitout, fitdat = isolated_peak_fit(self.centdat[:, 0], self.centdat[:, 1], psfun)
fitout = fitout[:, 0]
resolution = self.centdat[np.argmax(self.centdat[:, 1]), 0] / fitout[0]
error = np.sum((fitdat - self.centdat[:, 1]) * (fitdat - self.centdat[:, 1]))
self.fitmid = fitout[1]
self.fitsig = fitout[0]
# Show the results
print(self.fitmid, self.fitsig)
if self.flipflag == 0:
self.outmzsig.SetValue(str(fitout[0]))
else:
self.outdtsig.SetValue(str(fitout[0]))
self.ctlsigguess.SetValue(str(fitout[0]))
self.resbox.SetValue(str(resolution))
self.plot1.plotrefreshtop(self.centdat[:, 0], self.centdat[:, 1], title="Data", xlabel="m/z (Th)",
ylabel="Normalized Intensity", zoom="span")
self.plot1.plotadd(self.centdat[:, 0], fitdat, "blue", "Peak Shape Guess", nopaint=False)
self.errorbox.SetValue(str(error))
pass