def fit_signal_gauss(binning):
xlim = gl.corrAx.get_xlim()
ylim = gl.corrAx.get_ylim()
disp.refresh_display(binning)
if gl.boolSig == True:
lborder = int(gl.fitRangeLeftEntry.get())
rborder = int(gl.fitRangeRightEntry.get())
sig_start = 2.
x_fit = np.arange(lborder, rborder + 1, 1)
y_fit = gl.g2_sig[lborder:rborder + 1]
med_start = np.argmax(y_fit) + lborder
amp_start = np.argmax(y_fit) - 1.
popt, pcov = curve_fit(gauss,
x_fit,
y_fit,
p0=[amp_start, med_start, sig_start, 1.])
perr = np.sqrt(np.diag(pcov))
gl.x_plot = np.arange(lborder, rborder + 1, 0.01)
try:
gl.corrAx.gl.fit_plot.remove()
except:
pass
gl.fit_plot = gl.corrAx.plot(gl.x_plot,
gauss(gl.x_plot, *popt),
color="red")
gl.corrAx.set_xlim(xlim)
gl.corrAx.set_ylim(ylim)
I, dI = get_integral(popt, perr, binning)
gl.intValLabel.config(text="{:.1f} +/- {:.1f} fs".format(I, dI))
#gl.timeResValLabel.config(text="{:.2f} +/- {:.2f} ns".format(1e9*binning*popt[2], binning*perr[2]))
else:
print("No Signal measurement available")
def fit_difference_shape(binning):
make_peak_fu()
xlim = gl.corrAx.get_xlim()
ylim = gl.corrAx.get_ylim()
disp.refresh_display(binning)
if gl.boolSig == True and gl.boolRef == True:
lborder = int(gl.fitRangeLeftEntry.get())
rborder = int(gl.fitRangeRightEntry.get())
sig_start = 2.
x_fit = np.arange(lborder, rborder + 1, 1)
y_fit = gl.g2_diff[lborder:rborder + 1]
med_start = np.argmax(y_fit) + lborder
amp_start = np.argmax(y_fit) - 1.
popt, pcov = curve_fit(peak_fu,
x_fit,
y_fit,
p0=[amp_start, med_start, 1.])
perr = np.sqrt(np.diag(pcov))
gl.x_plot = np.arange(lborder, rborder + 1, 0.01)
try:
gl.corrAx.gl.fit_plot.remove()
except:
pass
gl.fit_plot = gl.corrAx.plot(gl.x_plot,
peak_fu(gl.x_plot, *popt),
color="orange")
gl.corrAx.set_xlim(xlim)
gl.corrAx.set_ylim(ylim)
I = 1e6 * 1e9 * binning * popt[0]
dI = 1e6 * 1e9 * binning * perr[0]
gl.intValLabel.config(text="{:.1f} +/- {:.1f} fs".format(I, dI))
#gl.timeResValLabel.config(text="{:.2f} +/- {:.2f} ns".format(1e9*binning*popt[2], 1e9*binning*perr[2]))
else:
print("No Difference measurement available")
def switch_logyButton():
if gl.boolRMSlogy == False:
gl.boolRMSlogy = True
RMSlogyButton.config(text="RMS log(y) ON", bg="#f77059")
else:
gl.boolRMSlogy = False
RMSlogyButton.config(text="RMS log(y) OFF", bg="#fad1ca")
xlim = gl.corrAx.get_xlim()
ylim = gl.corrAx.get_ylim()
disp.refresh_display(binning=float(binningoptions[binning.get()]))
gl.corrAx.set_xlim(xlim)
gl.corrAx.set_ylim(ylim)
def cumulate_signal_per_file(binning):
bas.reset_values()
if gl.boolSig == True:
print("Analyze Signal ...")
file_list_sig()
gl.isum_sig = np.zeros(len(np.loadtxt(gl.files_sig[0])[:, 1]))
for i in range(0, len(gl.files_sig)):
get_photon_rate_sig(i, binning)
single_RMS_sig(i, binning)
single_RMS_sig_exp(i, binning)
cumulative_g2_per_file_sig(i)
cumulative_RMS_sig(binning, method=1)
cumulative_RMS_sig_exp(i, binning)
gl.g2_sig = get_g2_per_file_sig()
bas.correlate_shapes()
if gl.boolRef == True:
print("Analyze Reference ...")
file_list_ref()
gl.isum_ref = np.zeros(len(np.loadtxt(gl.files_ref[0])[:, 1]))
for i in range(0, len(gl.files_ref)):
get_photon_rate_ref(i, binning)
single_RMS_ref(i, binning)
single_RMS_ref_exp(i, binning)
cumulative_g2_per_file_ref(i)
cumulative_RMS_ref(binning, method=1)
cumulative_RMS_ref_exp(i, binning)
if gl.boolSig == True:
cumulative_RMS_diff(binning, method=1)
cumulative_RMS_diff_exp(i)
gl.g2_ref = get_g2_per_file_ref()
if gl.boolSig == True:
gl.g2_diff = get_cumulative_g2_diff_per_file()
fft(binning)
disp.refresh_display(binning)
print("Done\n")