offsets,
nsamp=nsamp)
print "loaded waveforms in", (time.time() - start), "seconds"
sweep_data = sweeps.do_prepared_sweep(ri,
nchan_per_step=atonce,
reads_per_step=1)
orig_sweep_data = sweep_data
meas_cfs = []
idxs = []
delays = []
for m in range(len(f0s)):
fr, s21, errors = sweep_data.select_by_freq(f0s[m])
thiscf = f0s[m]
res = fit_best_resonator(
fr[1:-1], s21[1:-1],
errors=errors[1:-1]) #Resonator(fr,s21,errors=errors)
delay = res.delay
delays.append(delay)
s21 = s21 * np.exp(2j * np.pi * res.delay * fr)
res = fit_best_resonator(fr, s21, errors=errors)
fmin = fr[np.abs(s21).argmin()]
print "s21 fmin", fmin, "original guess", thiscf, "this fit", res.f_0, "delay", delay, "resid delay", res.delay
if use_fmin:
meas_cfs.append(fmin)
else:
if abs(res.f_0 - thiscf) > 0.1:
if abs(fmin - thiscf) > 0.1:
print "using original guess"
meas_cfs.append(thiscf)
else:
print "setting attenuator to",attenlist[0]
ri.set_dac_attenuator(attenlist[0])
f0binned = np.round(f0s*nsamp/512.0)*512.0/nsamp
f0binned = f0binned*source_on_freq_scale
measured_freqs = sweeps.prepare_sweep(ri,f0binned,offsets,nsamp=nsamp)
print "loaded waveforms in", (time.time()-start),"seconds"
sweep_data = sweeps.do_prepared_sweep(ri, nchan_per_step=atonce, reads_per_step=1)
orig_sweep_data = sweep_data
meas_cfs = []
idxs = []
delays = []
for m in range(len(f0s)):
fr,s21,errors = sweep_data.select_by_freq(f0s[m])
thiscf = f0s[m]*source_on_freq_scale
res = fit_best_resonator(fr[1:-1],s21[1:-1],errors=errors[1:-1]) #Resonator(fr,s21,errors=errors)
delay = res.delay
delays.append(delay)
s21 = s21*np.exp(2j*np.pi*res.delay*fr)
res = fit_best_resonator(fr,s21,errors=errors)
fmin = fr[np.abs(s21).argmin()]
print "s21 fmin", fmin, "original guess",thiscf,"this fit", res.f_0, "delay",delay,"resid delay",res.delay
if use_fmin:
meas_cfs.append(fmin)
else:
if abs(res.f_0 - thiscf) > max_fit_error:
if abs(fmin - thiscf) > max_fit_error:
print "using original guess"
meas_cfs.append(thiscf)
else:
print "using fmin"
if source_state == 'on':
mmw_source_modulation_freq = source_on()
else:
mmw_source_modulation_freq = source_off()
df.log_hw_state(ri)
sweep_data = sweeps.do_prepared_sweep(ri, nchan_per_step=f0s.size, reads_per_step=2)
df.add_sweep(sweep_data)
meas_cfs = []
idxs = []
for m in range(len(f0s)):
fr, s21, errors = sweep_data.select_by_freq(f0s[m])
thiscf = f0s[m]
s21 = s21 * np.exp(2j * np.pi * delay * fr)
res = fit_best_resonator(fr, s21, errors=errors) # Resonator(fr,s21,errors=errors)
fmin = fr[np.abs(s21).argmin()]
print "s21 fmin", fmin, "original guess", thiscf, "this fit", res.f_0
if 'a' in res.result.params or use_fmin: #k != 0 or use_fmin:
print "using fmin"
meas_cfs.append(fmin)
else:
if abs(res.f_0 - thiscf) > max_fit_error:
if abs(fmin - thiscf) > max_fit_error:
print "using original guess"
meas_cfs.append(thiscf)
else:
print "using fmin"
meas_cfs.append(fmin)
else:
print "using this fit"
else:
mmw_source_modulation_freq = source_off()
df.log_hw_state(ri)
sweep_data = sweeps.do_prepared_sweep(ri,
nchan_per_step=f0s.size,
reads_per_step=2)
df.add_sweep(sweep_data)
meas_cfs = []
idxs = []
for m in range(len(f0s)):
fr, s21, errors = sweep_data.select_by_freq(f0s[m])
thiscf = f0s[m]
s21 = s21 * np.exp(2j * np.pi * delay * fr)
res = fit_best_resonator(
fr, s21, errors=errors) # Resonator(fr,s21,errors=errors)
fmin = fr[np.abs(s21).argmin()]
print "s21 fmin", fmin, "original guess", thiscf, "this fit", res.f_0
if 'a' in res.result.params or use_fmin: #k != 0 or use_fmin:
print "using fmin"
meas_cfs.append(fmin)
else:
if abs(res.f_0 - thiscf) > max_fit_error:
if abs(fmin - thiscf) > max_fit_error:
print "using original guess"
meas_cfs.append(thiscf)
else:
print "using fmin"
meas_cfs.append(fmin)
else:
print "using this fit"