def notch(f_data, z_data, progress_bar, var_col=0, f_col=1, z_col=2):
#progress bar
progress_bar.setMaximum(5)
i_progress = 0
progress_bar.setValue(i_progress)
QApplication.processEvents()
#calibration
delay, amp_norm, alpha, fr, Qr, A2, frcal = rtx.do_calibration(
f_data, z_data[0], ignoreslope=True)
i_progress += 1
progress_bar.setValue(i_progress)
QApplication.processEvents()
#normalization
z_norm = rtx.do_normalization(f_data, z_data, delay, amp_norm, alpha, A2,
frcal)
i_progress += 1
progress_bar.setValue(i_progress)
QApplication.processEvents()
#circlefit
parameters = map(
lambda x: rtx.circlefit(f_data, z_norm[x], calc_errors=True),
xrange(len(z_norm)))
i_progress += 1
progress_bar.setValue(i_progress)
QApplication.processEvents()
#simulation
z_data_sim = np.array(
map(
lambda p: to.S21(f_data,
fr=p["fr"],
Qr=p["Qr"],
Qc=p["absQc"],
phi=p["phi0"],
a=amp_norm,
alpha=alpha,
delay=delay), parameters))
#residual values
residual_amp = np.absolute(z_norm) - np.absolute(z_data_sim)
residual_phase = np.angle(z_norm) - np.angle(z_data_sim)
i_progress += 1
progress_bar.setValue(i_progress)
QApplication.processEvents()
return {
"z_sim": z_data_sim,
"res_amp": residual_amp,
"res_phase": residual_phase,
"parameters": parameters,
"amp_norm": amp_norm,
"alpha": alpha,
"delay": delay
}
def notch(f_data,z_data,progress_bar,var_col=0,f_col=1,z_col=2):
#progress bar
progress_bar.setMaximum(5)
i_progress=0
progress_bar.setValue(i_progress)
QApplication.processEvents()
#calibration
delay,amp_norm,alpha,fr,Qr,A2,frcal=rtx.do_calibration(f_data,z_data[0],ignoreslope=True)
i_progress+=1
progress_bar.setValue(i_progress)
QApplication.processEvents()
#normalization
z_norm = rtx.do_normalization(f_data,z_data,delay,amp_norm,alpha,A2,frcal)
i_progress+=1
progress_bar.setValue(i_progress)
QApplication.processEvents()
#circlefit
parameters = map(lambda x: rtx.circlefit(f_data,z_norm[x],calc_errors=True), xrange(len(z_norm)))
i_progress+=1
progress_bar.setValue(i_progress)
QApplication.processEvents()
#simulation
z_data_sim = np.array(map(lambda p: to.S21(f_data,fr=p["fr"],Qr=p["Qr"],Qc=p["absQc"],
phi=p["phi0"],a=amp_norm,alpha=alpha,delay=delay), parameters))
#residual values
residual_amp = np.absolute(z_norm) - np.absolute(z_data_sim)
residual_phase = np.angle(z_norm) - np.angle(z_data_sim)
i_progress+=1
progress_bar.setValue(i_progress)
QApplication.processEvents()
return {"z_sim":z_data_sim,
"res_amp":residual_amp,
"res_phase":residual_phase,"parameters":parameters,
"amp_norm":amp_norm,"alpha":alpha,
"delay":delay}
##prepare data (remove delay, and normalize)
# step2#
z_data = rtx.do_normalization(f_data, z_data_raw, delay, amp_norm, alpha, A2, frcal)
print "Step 2: normalization finished"
# ----#
## optional plotting
# rtx.plot(f_data,z_data,'logamp')
# rtx.plot(f_data,z_data,'phase')
# rtx.plot(f_data,z_data,'circle')
##calculate Q values etc.
# step3# (function assumes normalized data without cable delay or phase offsets, use step 1 and 2 to remove these)
# results = rtx.circlefit(f_data,z_data,fr,Qr,refine_results=False,calc_errors=True) # use this for calculation with errors
results = rtx.circlefit(
f_data, z_data, fr, Qr, refine_results=False, calc_errors=True
) # calculation without errors (faster)
print "Step 3: circlefit finished"
# ----#
# optional data output (more available, see source)
print "Fit results:"
print results
# optional: least square fit of the entire model (slow)
# you have to give start values
# results2 = rtx.fit_S21data(f_data,z_data_raw,amp_norm,alpha,delay,Qr,absQc,phi0,fr,maxiter=0)
# optinal: plot function with fitted parameters
z_data_sim = np.array(
