def do_fit_neon(self):
self.peak_threshold = self.calib_threshold_entry.value()
self.peak_threshold = float(self.peak_threshold)
self.do_acquisition()
self.neon_fit_param, self.neon_fit_data = pressure.neon_fit(
self.wavelength, self.intensity, self.peak_threshold)
if self.fit_plot is not None:
self.ax.lines.remove(self.fit_plot_[0])
self.fit_plot_ = self.ax.plot(self.wavelength,
self.neon_fit_data,
"b-",
lw=1.5)
self.fit_plot = 1
self.canvas.draw()
#Getting the fitted peaks positions
n = len(self.neon_fit_param.keys()) - 1
n = n / 4 #Number of peaks
self.neon_peaks_positions = [] #fitted positions in wavelength
for i in range(n):
x = self.neon_fit_param["X%d" % i].value
self.neon_peaks_positions.append(x)
self.neon_peaks_positions = sorted(self.neon_peaks_positions)
self.true_neon_wl_index = peak.get_index_from_values(
__NEON_PEAKS__, self.neon_peaks_positions)
self.neon_true_wavelength = __NEON_PEAKS__[self.true_neon_wl_index]
self.measured_wl_pixel = peak.get_index_from_values(
self.wavelength, self.neon_peaks_positions)
self.calibrated_coefficients = pressure.calibration_coefficients(
self.measured_wl_pixel, self.neon_true_wavelength)
def get_maxima(x, y, threshold=0.3):
ind = peak.indexes(y, thres=threshold, min_dist=20)
x_peaks = peak.interpolate(x, y, ind, width=20,
func=peak.lorentzian_fit) #lorentzian_fit
new_ind = peak.get_index_from_values(x, x_peaks)
y_peaks = y[new_ind]
return (x_peaks, y_peaks)
def neon_fit(x,y,threshold): ind = peak.indexes(y, thres=threshold, min_dist=30) x_peaks = peak.interpolate(x,y,ind, width=15, func = peak.lorentzian_fit) new_ind = peak.get_index_from_values(x, x_peaks) y_peaks = y[new_ind] param_init = neon_init(x_peaks, y_peaks) result = minimize(objective, param_init, args=(y,x, "neon")) y = neon_model(result.params, x) return result.params, y
def neon_fit(x, y, threshold):
ind = peak.indexes(y, thres=threshold, min_dist=30)
x_peaks = peak.interpolate(x, y, ind, width=15, func=peak.lorentzian_fit)
new_ind = peak.get_index_from_values(x, x_peaks)
y_peaks = y[new_ind]
param_init = neon_init(x_peaks, y_peaks)
result = minimize(objective, param_init, args=(y, x, "neon"))
y = neon_model(result.params, x)
return result.params, y
def do_fit_neon(self):
self.peak_threshold = self.calib_threshold_entry.value()
self.peak_threshold = float(self.peak_threshold)
self.do_acquisition()
self.neon_fit_param, self.neon_fit_data = pressure.neon_fit(self.wavelength,self.intensity, self.peak_threshold)
if self.fit_plot is not None:
self.ax.lines.remove(self.fit_plot_[0])
self.fit_plot_ = self.ax.plot(self.wavelength, self.neon_fit_data, "b-", lw=1.5)
self.fit_plot = 1
self.canvas.draw()
#Getting the fitted peaks positions
n = len(self.neon_fit_param.keys())-1
n = n/4#Number of peaks
self.neon_peaks_positions = []#fitted positions in wavelength
for i in range(n):
x = self.neon_fit_param["X%d"%i].value
self.neon_peaks_positions.append(x)
self.neon_peaks_positions = sorted(self.neon_peaks_positions)
self.true_neon_wl_index = peak.get_index_from_values(__NEON_PEAKS__, self.neon_peaks_positions)
self.neon_true_wavelength = __NEON_PEAKS__[self.true_neon_wl_index]
self.measured_wl_pixel = peak.get_index_from_values(self.wavelength, self.neon_peaks_positions)
self.calibrated_coefficients = pressure.calibration_coefficients(self.measured_wl_pixel, self.neon_true_wavelength)
# fit_param,fit_data = neon_fit(wavelength,intensity, 0.13) fit_param,fit_data = neon_fit(wavelength,intensity, 0.055) # f=figure() # ax=f.add_subplot(111) # ax.plot(wavelength, intensity, "ko") # ax.plot(wavelength, fit_data, "r-") # ax.bar(WL, INT, width=0.3, color="b") # show() nbr_peaks = len(fit_param.keys()) -1 nbr_peaks = nbr_peaks/4 fitted_wl = [] fitted_in = [] for i in range(nbr_peaks): wl = fit_param["X%d"%i].value inten = neon_model(fit_param, wl) fitted_wl.append(wl) fitted_in.append(inten) fitted_wl = np.array(fitted_wl) fitted_in = np.array(fitted_in) f2=figure() ax2=f2.add_subplot(111) ax2.plot(wavelength, intensity, "k-") # ax2.bar(fitted_wl, fitted_in, width=0.2, color="red") ax2.plot(wavelength, fit_data, "r-") for i in range(len(WL)): ax2.axvline(WL[i],color="g") true_ind = peak.get_index_from_values(WL, fitted_wl) true_wl = WL[true_ind] for i in range(len(true_wl)): ax2.text(fitted_wl[i], 0.2, str(round(true_wl[i],3))) show()
# fit_param,fit_data = neon_fit(wavelength,intensity, 0.13)
fit_param, fit_data = neon_fit(wavelength, intensity, 0.055)
# f=figure()
# ax=f.add_subplot(111)
# ax.plot(wavelength, intensity, "ko")
# ax.plot(wavelength, fit_data, "r-")
# ax.bar(WL, INT, width=0.3, color="b")
# show()
nbr_peaks = len(fit_param.keys()) - 1
nbr_peaks = nbr_peaks / 4
fitted_wl = []
fitted_in = []
for i in range(nbr_peaks):
wl = fit_param["X%d" % i].value
inten = neon_model(fit_param, wl)
fitted_wl.append(wl)
fitted_in.append(inten)
fitted_wl = np.array(fitted_wl)
fitted_in = np.array(fitted_in)
f2 = figure()
ax2 = f2.add_subplot(111)
ax2.plot(wavelength, intensity, "k-")
# ax2.bar(fitted_wl, fitted_in, width=0.2, color="red")
ax2.plot(wavelength, fit_data, "r-")
for i in range(len(WL)):
ax2.axvline(WL[i], color="g")
true_ind = peak.get_index_from_values(WL, fitted_wl)
true_wl = WL[true_ind]
for i in range(len(true_wl)):
ax2.text(fitted_wl[i], 0.2, str(round(true_wl[i], 3)))
show()
