def plot_pulse_cal_dac_offsets_vs_days(s, decimation=21):
data_directory = ["", "Brooks", "pulseCal", "dac_step_offsets"]
cxn = s._cxn
d = dvw(data_directory, cxn)
datasets = [29]
datasets_ref = np.linspace(20, 39, 20)
# datasets_ref = [20]
datasets = np.linspace(40, 279, 240)
markers = ["r-", "b-", "g-", "m-", "c-", "k-", "y-"]
diff_chans = ["Diff+", "Diff-"]
for data_ref in datasets_ref:
data_ref = np.int(data_ref)
for diff_chan in diff_chans:
count = 0
fig = plt.figure()
for ds in datasets:
ds = np.int(ds)
print "ds: {}; %20: {}".format(ds, ds % 20)
if (ds % 20) + 20 == data_ref:
data = d[ds]
print "current dataset: {}".format(ds)
data_times = data.get_column("Time")["us"]
data_voltage = data.get_column(diff_chan)["V"]
data_voltage, amp, offset = normalize_pulse(data_voltage, percent=1.0 / 15)
data_voltage = smoothing(data_voltage, decimation)
data_times = smoothing(data_times, decimation)
ref_data = d[np.int(data_ref)]
ref_voltage = ref_data.get_column(diff_chan)["V"]
ref_voltage, amp, offset = normalize_pulse(ref_voltage, percent=1.0 / 15)
ref_voltage = smoothing(ref_voltage, decimation)
plt.plot(
data_times,
data_voltage - ref_voltage,
markers[count % 7],
label="{}, step#: {}".format(diff_chan, count),
)
gc.collect()
count += 1
plt.title(
"Data-Reference vs Time. ACQ:RESP: {}, low: {}; high: {}".format(
data.parameters["scope_response_filter"],
data.parameters["low_dac_amp"],
data.parameters["high_dac_amp"],
)
)
plt.xlabel("Time (us)")
plt.ylabel("% of step")
plt.axis([-100, 700, -0.05, 0.05])
plt.tight_layout()
plt.legend(loc=4, numpoints=1)
fig.savefig("{}; low_amp = {}.png".format(diff_chan, data.parameters["low_dac_amp"]), bbox_inches="tight")
plt.close("all")
def plot_pulse_cals_vs_channels(s):
data_directory = ["", "Brooks", "pulseCal", "AgilentDSA90804A"]
cxn = s._cxn
d = dvw(data_directory, cxn)
datasets = [1, 2, 3, 4]
plt.figure()
plt.title("Calibration Pulse Different sample rates: Ch1")
markers = ["r-", "b-", "g-", "m-"]
count = 0
for ds in datasets:
data = d[ds]
data_times = data.get_column("Time")["ns"]
data_voltage = data.get_column("Voltage")["V"]
plt.plot(
data_times / 1.0e3,
data_voltage * 1.0e3 / 5,
markers[count],
label="ch{}".format(data.parameters["channel"]),
)
count += 1
plt.xlabel("Time (us)")
plt.ylabel("Voltage (% of step)")
plt.legend(loc=4, numpoints=1)
def plot_pulse_cals_vs_y_pos(s):
data_directory = ["", "Brooks", "pulseCal", "AgilentDSA90804A"]
cxn = s._cxn
d = dvw(data_directory, cxn)
datasets = [104, 105, 106, 107, 108, 109, 110, 111, 112]
plt.figure()
plt.title("Calibration Pulse Different y_offset: Ch1")
markers = ["r-", "b-", "g-", "m-", "c-", "k-", "y-", "r--", "b--"]
count = 0
for ds in datasets:
data = d[ds]
data_times = data.get_column("Time")["ns"]
data_voltage = data.get_column("Voltage")["V"]
plt.plot(
data_times[::2] / 1.0e3,
data_voltage[::2] * 1.0e3,
markers[count],
label="{} V".format(data.parameters["y_position_v"]),
)
gc.collect()
count += 1
plt.xlabel("Time (us)")
plt.ylabel("Voltage (mV)")
plt.legend(loc=4, numpoints=1)
def plot_pulse_cal_steps(s):
data_directory = ["", "Brooks", "pulseCal", "AgilentDSA90804A", "DAC_source"]
cxn = s._cxn
d = dvw(data_directory, cxn)
datasets = np.linspace(4, 299, 296)
# datasets = np.linspace(4, 20, 17)
plt.figure()
plt.title("Calibration Pulse Repetitions, Fs: 1e9Hz, 50mV/div: Ch1")
markers = ["r-", "b-", "g-", "m-", "c-", "k-", "y-", "r--"]
count = 0
for ds in datasets:
if ds % 10 == 4:
ds = np.int(ds)
data = d[ds]
print "current dataset: {}".format(ds)
if data.parameters["dac_low_amp"] == -0.5:
print "found match, dataset: {}".format(ds)
data_times = data.get_column("Time")["ns"]
data_voltage = data.get_column("Voltage")["V"]
data_voltage -= np.mean(data_voltage)
plt.plot(
data_times[::2] / 1.0e3, data_voltage[::2] * 1.0e3, markers[count % 7], label="{}".format(count)
)
gc.collect()
count += 1
plt.xlabel("Time (us)")
plt.ylabel("Voltage (mV)")
plt.legend(loc=4, numpoints=1)
def plot_pulse_cals_vs_x_axis(s):
data_directory = ["", "Brooks", "pulseCal", "AgilentDSA90804A"]
cxn = s._cxn
d = dvw(data_directory, cxn)
datasets = [24, 25, 26, 27, 28, 29, 30]
plt.figure()
plt.title("Calibration Pulse Different x-scales, 1e9Hz, 50mV/div: Ch1")
markers = ["r-", "b-", "g-", "m-", "c-", "k-", "y-", "r--"]
count = 0
for ds in datasets:
data = d[ds]
data_times = data.get_column("Time")["ns"]
data_voltage = data.get_column("Voltage")["V"]
plt.plot(
data_times[::2] / 1.0e3,
data_voltage[::2] * 1.0e3,
markers[count],
label="{} s/div".format(data.parameters["horizontal_scale"]),
)
gc.collect()
count += 1
plt.xlabel("Time (us)")
plt.ylabel("Voltage (mV)")
plt.legend(loc=4, numpoints=1)
def plot_pulse_cals_vs_sample_rate2(s):
data_directory = ["", "Brooks", "pulseCal", "AgilentDSA90804A"]
cxn = s._cxn
d = dvw(data_directory, cxn)
datasets = [11, 12, 13, 14, 15]
plt.figure()
plt.title("Calibration Pulse Different sample rates: Ch1")
markers = ["r-", "b-", "g-", "m-", "c-", "k-"]
count = 0
for ds in datasets:
data = d[ds]
data_times = data.get_column("Time")["ns"]
data_voltage = data.get_column("Voltage")["V"]
plt.plot(
data_times[::2] / 1.0e3,
data_voltage[::2] * 1.0e3,
markers[count],
label="Fs: {} Hz".format(data.parameters["sample_rate"]),
)
gc.collect()
count += 1
plt.xlabel("Time (us)")
plt.ylabel("Voltage (mV)")
plt.legend(loc=4, numpoints=1)
def plot_pulse_cals_vs_scales(s):
data_directory = ["", "Brooks", "pulseCal", "AgilentDSA90804A"]
cxn = s._cxn
d = dvw(data_directory, cxn)
datasets = [16, 17, 18, 19, 20, 21, 22, 23]
plt.figure()
plt.title("Calibration Pulse Different scaled, 1e9Hz: Ch1")
markers = ["r-", "b-", "g-", "m-", "c-", "k-", "y-", "r--"]
count = 0
for ds in datasets:
data = d[ds]
data_times = data.get_column("Time")["ns"]
data_voltage = data.get_column("Voltage")["V"]
plt.plot(
data_times[::2] / 1.0e3,
data_voltage[::2] * 1.0e3,
markers[count],
label="{} V/div".format(data.parameters["y_scale_v_per_div"]),
)
gc.collect()
count += 1
plt.xlabel("Time (us)")
plt.ylabel("Voltage (mV)")
plt.legend(loc=4, numpoints=1)
def plotPhaseCalPowers(s, IQ = True, chooseCal = False):
dataDir = ['', 'Brooks', 'parAmp', 'impaNGC_CuBox_CuAuPCB', 'longZParamp', '160604'] #longZParamp in NGC Cu Box, CuAuPCB
cxn = s._cxn
d = dvw(dataDir,cxn)
dataSets = d.keys()
cm = plt.cm.get_cmap('RdYlBu')
for dataSet in dataSets:
if dataSet[-3::] == 'dBm': #then scan is at 18dBm
data = d[dataSet]
color = np.float(string.split(data.parameters.get('loPower'))[0])
print color
phase = IQtoPhase(data['Q']/data['I'])
if IQ:
sc = plt.scatter(data['I'], data['Q'], c = np.zeros(len(phase)) + color, vmin = 0, vmax = 20, cmap = cm, edgecolors = 'none' )
else:
sc = plt.scatter(data['FluxV [V]'], phase, c = np.zeros(len(phase)) + color, vmin = 0, vmax = 20, cmap = cm, edgecolors = 'none' )
plt.colorbar(sc, label = 'CW Tone Power (dBm)')
if IQ:
plt.title('IQ Plane vs CW Tone Power (dBm) @ 4.8 GHz',fontsize=20)
plt.ylabel('Q (V)')
plt.xlabel('I (V)')
else:
plt.title('Phase (rad) vs Flux Voltage (V) @ 4.8 GHz',fontsize=20)
plt.ylabel('Phase (rad)')
plt.xlabel('Flux Voltage (V)')
plt.show()
def plotPhaseCalFreqs(sample, IQ = True, freq = None, chooseCal = False, dataDir = None):
cxn = sample._cxn
if dataDir == None:
d = dvw(sample,cxn)
else:
d = dvw(dataDir,cxn)
dataSets = d.keys()
dataSets = dataSets[38::-1]
cm = plt.cm.get_cmap('RdYlBu')
freqtemp = freq
if freq == None:
freqtemp = 1.
for dataSet in dataSets:
if dataSet[-3::] == 'GHz' and (np.int(np.float(dataSet[-7:-4])*10.0) == np.int(10.0*freqtemp) or freq == None): #then scan is at 18dBm
data = d[dataSet]
print dataSet
color = np.float(data.parameters.get('loFreq')['Hz'])/1000000000.0
pwr = np.float(data.parameters.get('loPower')['dBm'])
print color
phase = IQtoPhase(data['Q']/data['I'])
# DCIQcofit(sample, dataSet = dataSet, plot = True,dataDir = None, useReg = False)
if IQ:
sc = plt.scatter(data['I'], data['Q'], c = np.zeros(len(phase)) + color, vmin = 4, vmax = 6.5, cmap = cm, edgecolors = 'none' )
else:
sc = plt.scatter(data['FluxV [V]'], phase, c = np.zeros(len(phase)) + color, vmin = 4, vmax = 6.5, cmap = cm, edgecolors = 'none' )
plt.colorbar(sc, label = 'CW Tone Frequency (GHz)')
if IQ:
plt.title('IQ Plane: %s GHz @ %s dBm'%(color, pwr),fontsize=20)
plt.ylabel('Q (V)')
plt.xlabel('I (V)')
else:
plt.title('Phase (rad) vs Flux Voltage (V): 18dBm Drive',fontsize=20)
plt.xlabel('Flux Voltage (V)')
plt.ylabel('Phase (rad)')
plt.show()
def plot_pulse_cal_dac_offsets(s, decimation=21):
data_directory = ["", "Brooks", "pulseCal", "dac_step_offsets"]
cxn = s._cxn
d = dvw(data_directory, cxn)
datasets = [29]
datasets.append(np.linspace(20, 28, 9))
datasets.append(np.linspace(30, 39, 10))
datasets = np.hstack(datasets)
datasets = [21, 41]
markers = ["r-", "b-", "g-", "m-", "c-", "k-", "y-"]
diff_chans = ["Diff+", "Diff-"]
for diff_chan in diff_chans:
count = 0
plt.figure()
for ds in datasets:
ds = np.int(ds)
data = d[ds]
print "current dataset: {}".format(ds)
data_times = data.get_column("Time")["us"]
data_voltage = data.get_column(diff_chan)["V"]
data_voltage, amp, offset = normalize_pulse(data_voltage, percent=1.0 / 15)
data_voltage = smoothing(data_voltage, decimation)
data_times = smoothing(data_times, decimation)
if count == 0:
ref_voltage = data_voltage
plt.plot(
data_times,
data_voltage - ref_voltage,
markers[count % 7],
label="{}, step#: {}".format(diff_chan, count),
)
gc.collect()
count += 1
plt.title(
"0.1Dac_amp Step, diff_offset, Fs: 1e9Hz, 50mV/div: Ch1, {} resp".format(
data.parameters["scope_response_filter"]
)
)
plt.xlabel("Time (us)")
plt.ylabel("% of step")
plt.axis([-100, 700, -0.15, 0.15])
plt.tight_layout()
plt.legend(loc=4, numpoints=1)
def plot_pulse_cal_attens(s, decimation=55):
data_directory = ["", "Brooks", "pulseCal", "fixed_sensitivity_atten_rfpg"]
cxn = s._cxn
d = dvw(data_directory, cxn)
datasets = [2, 12, 10, 8, 6, 4] # guassian
datasets = [1, 11, 9, 7, 5, 3] # flat
plt.figure()
markers = ["r-", "b-", "g-", "m-", "c-", "k-", "y-"]
count = 0
for ds in datasets:
ds = int(ds)
data = d[ds]
print "current dataset: {}".format(ds)
data_times = data.get_column("Time")["us"]
data_voltage = data.get_column("Voltage")["V"]
data_voltage, amp, offset = normalize_pulse(data_voltage, percent=1.0 / 15)
data_voltage = smoothing(data_voltage, decimation)
data_times = smoothing(data_times, decimation)
if count == 0:
ref_voltage = data_voltage
plt.plot(
data_times,
data_voltage - ref_voltage,
markers[count % 7],
label="{} db".format(data.parameters["attenuation"]),
)
gc.collect()
count += 1
plt.title(
"Calibration Pulse w/ different atten, Fs: 1e9Hz, 50mV/div: Ch1, {} resp".format(
data.parameters["scope_response_filter"]
)
)
plt.xlabel("Time (us)")
plt.ylabel("% of step")
plt.axis([-100, 700, -0.15, 0.15])
plt.tight_layout()
plt.legend(loc=4, numpoints=1)
def plot_pulse_cal_repetitions(s):
data_directory = ["", "Brooks", "pulseCal", "AgilentDSA90804A"]
cxn = s._cxn
d = dvw(data_directory, cxn)
datasets = np.linspace(36, 97, 62)
plt.figure()
plt.title("Calibration Pulse Repetitions, Fs: 1e9Hz, 50mV/div: Ch1")
markers = ["r-", "b-", "g-", "m-", "c-", "k-", "y-", "r--"]
count = 0
for ds in datasets:
ds = np.int(ds)
data = d[ds]
data_times = data.get_column("Time")["ns"]
data_voltage = data.get_column("Voltage")["V"]
plt.plot(data_times[::2] / 1.0e3, data_voltage[::2] * 1.0e3, markers[count % 7], label="{}".format(count))
gc.collect()
count += 1
plt.xlabel("Time (us)")
plt.ylabel("Voltage (mV)")
plt.legend(loc=4, numpoints=1)
def checkStep(sample, stepDataSet, stepDataDir = None):
cxn = sample._cxn
# get calibration datasets
if stepDataDir == None:
d = dvw(sample, cxn)
else:
d = dvw(dataDir, cxn)
stepData = d[stepDataSet]
calDataSet = stepData.parameters['IQcalibrationDataSet']
calDataDir = stepData.parameters['IQcalibrationDir']
calD = dvw(calDataDir, cxn)
calData = calD[calDataSet]
fluxVcal = calData['FluxV']
Ical = calData['I']
Qcal = calData['Q']
# convert flux voltage from dmmV to DAC amp
fluxDACampCal = dmmVtoDACamp(fluxVcal * V, calData, useReg = False)
# convert DAC amp flux voltage to flux
fluxCal = dacAmpToFlux(fluxDACampCal, calData, useReg = False)
# compute Cal phase
phaseCal = IQtoPhase(Ical, Qcal)
times = stepData['Time']
fluxV = stepData['FluxV']
fluxVDAC = scopeVtoDACamp(fluxV * V, stepData, useReg = False)
flux = dacAmpToFlux(fluxVDAC, stepData, useReg = False)
I = stepData['I']
Q = stepData['Q']
phase = IQtoPhase(I, Q)
startI = stepData.parameters['startI']
endI = stepData.parameters['endI']
startQ = stepData.parameters['startQ']
endQ = stepData.parameters['endQ']
startDACamp = stepData.parameters['startDACamp']
endDACamp = stepData.parameters['endDACamp']
plt.figure()
plt.title('I step vs expected')
plt.plot(times, I, 'rs', label = 'response I')
plt.plot([times[0],times[-1]], [startI, startI], 'b-', linewidth = 5, label = 'Expected Start I')
plt.plot([times[0],times[-1]], [endI, endI], 'g-', linewidth = 5, label = 'Expected end I')
plt.legend(numpoints = 1, loc = 3)
plt.show()
plt.figure()
plt.title('Q step vs expected')
plt.plot(times, Q, 'rs', label = 'response Q')
plt.plot([times[0],times[-1]], [startQ, startQ], 'b-', linewidth = 5, label = 'Expected Start Q')
plt.plot([times[0],times[-1]], [endQ, endQ], 'g-', linewidth = 5, label = 'Expected end Q')
plt.legend(numpoints = 1, loc = 3)
plt.show()
plt.figure()
plt.title('fluxV step vs expected')
plt.plot(times, fluxVDAC, 'rs', label = 'response fluxV')
plt.plot([times[0],times[-1]], [startDACamp, startDACamp], 'b-', linewidth = 5, label = 'Expected Start fluxV')
plt.plot([times[0],times[-1]], [endDACamp,endDACamp], 'g-', linewidth = 5, label = 'Expected end fluxV')
plt.legend(numpoints = 1, loc = 3)
plt.show()
def plot_settle_datasets(sample, set_qubits=None, set_amps=None, first=False,
directories=None, dir_sets=None,
plot=False, frequencies=False, title='', autoscale=True,
labels=None, plot_axes=[1, 5e5, 90.0, 100.4],
use_legend=True, leg_loc=4):
if directories is None:
directories = [sample._dir]
colors = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
if dir_sets is not None:
for dir_set in dir_sets:
d = dvw(dir_set[0], sample._cxn)
data = d[dir_set[1]]
if 'Z Settle Time Long Rise' not in data.name:
raise Exception('Error, this is not Z-settle dataset. Requested'
'data set is: {}'.format(data.name))
qubit = data.parameters['measure'][0]
q = data.parameters[qubit]
# Get spec peaks
fit_gauss_max = lambda x, y: fitting.getMaxGauss(x, y,
fitToMax=True)
times, freqs_MHz = fitting.minima_cuts(data, 0, method=fit_gauss_max,
plot=False)
# offset datavault times by 50ns + spectroscopyLen/2
spectroscopylen = data.parameters[qubit]['spectroscopyLen']['ns']
times += 50 + spectroscopylen/2.
# get zfunc and inverse zfunc
Z = zfuncs.TransmonFrequency(q['zFuncAna'])
def fit_settle(ts, a0, t0, a1, t1, a2, t2, b):
outp = []
for t in ts:
outp.append((a0 * np.exp(-t/t0)) + (a1 * np.exp(-t/t1)) + (a2 * np.exp(-t/t2)) + b)
return np.array(outp)
# seed estimates [A0, T0, A1, T1, A2, T2, B]
# A0 ~ 5 MHz
# A1 ~ 5 MHz
# A2 ~ 1 MHz
# T0 ~ 10 nsec
# T1 ~ 1000 nsec
# T2 ~ 50000 nsec
# B ~ a frequency.
# fit qubit frequency vs time
popt1, pcov1 = curve_fit(fit_settle, times, freqs_MHz,
p0=[5., 10., 5., 100., 1000., 50000.,
freqs_MHz[-1]], maxfev=5200)
# calculate step amplitude in frequency
a0, t0, a1, t1, a2, t2, f_inf = popt1
ordering = t0 < t1 and t1 < t2
# if not ordering:
# raise Exception('Error! Time constants out of order! t0: {} ns;'
# ' t1: {} ns; t2: {} ns'.format(t0, t1, t2))
z_amp_inf = Z.freq_to_amp((f_inf / 1000.) * GHz)
# plot_times
plot_times = np.linspace(np.min(times), np.max(times), 1000)
# compute frequencies for fit plot
plot_frequencies = fit_settle(plot_times, a0, t0, a1, t1, a2, t2, f_inf)
# scale fit frequencies to
plot_z_amplitudes = Z.freq_to_amp((plot_frequencies / 1000.) * GHz)
# scale z_amps to fraction
plot_percent = plot_z_amplitudes * 100. / z_amp_inf
# Amplitude of step from DAC
z_amplitude = float(data.parameters['stack']['frame0']['arg']['z_amplitude'])
if np.abs(z_amplitude-z_amp_inf) > 0.03:
raise Exception('Error! Requested amplitude ({}) and measured '
'amplitude ({}) do not match!'
''.format(z_amplitude, z_amp_inf))
# Amplitude of step according to zfunc
zfunc_amplitudes = Z.freq_to_amp(freqs_MHz / 1000. * GHz)
# normalized z_func_amplitudes
zfunc_percent = 100. * zfunc_amplitudes / z_amp_inf
# convert settle amplitudes to fraction of step
f_step_amp = ((Z.amp_to_freq(0.0)['GHz']) * 1000.) - f_inf
a0 /= f_step_amp
a1 /= f_step_amp
a2 /= f_step_amp
f_step_amp = np.int(f_step_amp*10.)/10.
# data plot
if frequencies:
plt.semilogx(times, freqs_MHz, lw=0, marker='s',
label='{}: z_amp: {}; f_amp: {} MHz'.format(
qubit,
z_amplitude, f_step_amp))
plt.ylabel('Qubit Frequency (MHz)', fontsize=18)
if not autoscale:
plt.axis(plot_axes)
if len(dir_sets) == 1:
plt.semilogx(plot_times, plot_frequencies, lw=2, marker='s',
markeredgecolor='none')
else:
plt.semilogx(times, zfunc_percent, lw=0, marker='s',
label='{}; {} MHz'.format(
z_amplitude, f_step_amp))
plt.ylabel('Z Amp (% of step)', fontsize=18)
if not autoscale:
plt.axis(plot_axes)
# fit
# plt.semilogx(x_vals/1e3,y_vals*100, color = colors[counter], lw = 2, marker = None, label = 'z_step_amp: %s ;Tshort %s us; Ashort: %s; Tlong %s us; Along: %s'%(str(z_amplitude),str(shortTime), str(shortAmp), str(longTime), str(longAmp)))
if len(dir_sets) == 1:
plt.semilogx(plot_times, plot_percent, lw=2, marker='s', markeredgecolor='none')
if plot:
plt.title(title,
fontsize=18)
plt.xlabel('Time (ns)', fontsize=18)
plt.tick_params(labelsize=15)
if use_legend:
if labels is not None:
plt.legend(labels, numpoints=1, loc=leg_loc)
else:
plt.legend(numpoints=1, loc=leg_loc)
plt.tight_layout()
plt.show()
if False:
plt.figure()
plt.title(
'Settle Amplitude vs Step Amplitude: a0*exp(-t/t0) + a1*exp(-t/t1) + a2*exp(-t/t2)',
fontsize=11)
plt.ylabel('Settle amplitude (% of step)')
plt.xlabel('Step amplitude (DAC units)')
plt.plot(zamps, a0s, 'rs', label='a0s')
plt.plot(zamps, a1s, 'bs', label='a1s')
plt.plot(zamps, a2s, 'gs', label='a2s')
plt.legend(numpoints=1, loc=2)
plt.axis(plot_axes)
plt.tight_layout()
plt.show()
plt.figure()
plt.title(
'Settle Amplitude vs Step Amplitude: a0*exp(-t/t0) + a1*exp(-t/t1) + a2*exp(-t/t2)',
fontsize=11)
plt.ylabel('Settle amplitude (% of step)')
plt.xlabel('Step amplitude (DAC units)')
plt.semilogy(zamps, np.abs(a0s), 'rs', label='a0s')
plt.semilogy(zamps, np.abs(a1s), 'bs', label='a1s')
plt.semilogy(zamps, np.abs(a2s), 'gs', label='a2s')
plt.legend(numpoints=1, loc=3)
# plt.axis([-1.0,1.0, 0, 10])
plt.tight_layout()
plt.show()
plt.figure()
plt.title(
'Settle Times vs Step Amplitude: a0*exp(-t/t0) + a1*exp(-t/t1) + a2*exp(-t/t2)',
fontsize=11)
plt.ylabel('Settle Time nsec')
plt.xlabel('Step amplitude (DAC units)')
plt.semilogy(zamps, t0s, 'rs', label='t0s')
plt.semilogy(zamps, t1s, 'bs', label='t1s')
plt.semilogy(zamps, t2s, 'gs', label='t2s')
plt.legend(numpoints=1, loc=2)
# plt.axis([-1.0,1.0, 0, 10])
plt.tight_layout()
plt.show()
return times, zfunc_percent
def cal_IQ(sample, DACamps=np.linspace(-1.0,1.0,num=101)/4.0, loFreq=6.4 * GHz,
loPower=18.0 * dBm, averages=5, name='DC_calibration',
update=True, calScope=True, useScope=False):
if calScope and not useScope:
calibrateToDAC(sample, numPoints = 7, plot = False)
with labrad.connect() as cxn1:
independents = [('FluxV','V')]
dependents = [('I','','V'),('Q','','V')]
# create a DV reference for storing the summary data
dv1 = cxn1.data_vault
dv1.cd(sample._dir, True)
dv1.new(name, independents, dependents)
cxn = sample._cxn
adr = cxn.adr_server
ADR = adr.list_devices()
sample['ADR'] = '%s'%ADR
sample['averages'] = averages
adr.select_device()
temperatures = adr.temperatures()
sample['50K_Temp'] = temperatures[0]
sample['4K_Temp'] = temperatures[1]
sample['4K_Mag_Temp'] = temperatures[2]
ruox = adr.ruox_status()
sample['Ruox_Temp'] = ruox[0]
sample['Ruox_Resistance'] = ruox[1]
sample['Magnet_Current'] = adr.magnet_status()[0]
sample['Magnet_Voltage'] = adr.magnet_status()[1]
p1 = dv1.packet()
start = time.time()
sample['Start_time'] = start*1e9*ns
d = dvw(sample, cxn)
sample['IQcalibrationDir'] = sample._dir
sample['IQcalibrationDataSet'] = d[-1].name
sample['scanType'] = 'DC_calibration'
if useScope:
sample['DC_inst'] = 'Infiniium Scope'
else:
sample['DC_inst'] = 'Three Agilent DMMs'
#set FPGA voltage
DAC_DC(sample, amp = DACamps[0])
# Set up Hittite
config_Hittite(sample, loFreq = loFreq, loPower = loPower, outputState = 'ON')
if not useScope:
# Configure DMMS
dmm = cxn.agilent_34401a_dmm
dmms = dmm.list_devices()
for val in DACamps:
I = []
Q = []
fluxV = []
print 'Current DC voltage: ', val
# Set DAC Value
DAC_DC(sample, amp = val)
time.sleep(1)
if useScope:
data = InfiniiumScan(sample, averages = averages, config = True, record = False, name = 'Scopetrace', sampleRate = 40e9, horizScale = 1e-06, trig_level = 0.0, amp = val)
fluxV = np.array(data.T[1]).mean()
I = np.array(data.T[2]).mean()
Q = np.array(data.T[3]).mean()
#Take DMM points and average
else:
avgCount = 0
while avgCount < averages:
dmm.select_device(dmms[0][1])
I.append(dmm.voltage()['V'])
dmm.select_device(dmms[1][1])
Q.append(dmm.voltage()['V'])
dmm.select_device(dmms[2][1])
fluxV.append(dmm.voltage()['V'])
avgCount += 1
I = np.array(I).mean()
Q = np.array(Q).mean()
fluxV = np.array(fluxV).mean()
print 'fluxV: ',fluxV
print 'I: ',I
print 'Q: ',Q
currentTime = time.time()-start
dataPoint=[]
dataPoint.append(fluxV)
dataPoint.append(I)
dataPoint.append(Q)
dataPoint = np.array(dataPoint).T
dv1.add(dataPoint)
DCIQcofit(sample, dataSet = None, plot = False, dataDir = None, useReg = True)
for key in sample.keys():
p1.add_parameter(key, sample.__getitem__(key))
p1.send()
DAC_DC(sample, amp = 0.0)
config_Hittite(sample, loFreq = loFreq, loPower = loPower, outputState = 'OFF')
def IQtoFluxStep(s, dataDir = ['', 'Brooks', 'parAmp', 'impaNGC_CuBox_CuAuPCB', '151218', 'step'], box = ''):
if box == 'UCSB':
dataDir = ['', 'Brooks', 'parAmp', 'impaUCSBAlBox', '151118', 'calibration'] #UCSB IMPA Box
dataSet = 783
dataSet = 889
dataSet = 513
if box == 'NGC Cu':
dataDir = ['', 'Brooks', 'parAmp', 'impaNGC_CuBox_CuAuPCB', '151218', 'step']
dataSet = 783
if box == 'NGC Cu2':
dataDir = ['', 'Brooks', 'parAmp', 'impaNGC_CuBox_CuAuPCB', '160109', 'step']
dataSet = 853
# Get Calibration
fluxVScale, fluxVOffset, aI, aQ, thetaI, thetaQ, Z0, Ic = DCIQcofit(s, plot = False, bound = 0.1, dataDir = dataDir, box = box)
# poptQ, pcovQ, poptI, pcovI, popt1 = fitIQ(s, dataSet = 34, plot = False)
phi0 = 2.067833758 * (10**-15) #Wb
omega = 2 * np.pi * 4.2 * 10**9 # rad/sec
cap = 4.0 * 10**-12 # Farads
cxn = s._cxn
d = dvw(dataDir,cxn)
dataSets = d.keys()
# dataSets = dataSets[538:850]
# dataSets = dataSets[538:850]
# dataSets = dataSets[1220:1221]
if box == 'UCSB':
dataSets = dataSets[889:1254]
# dataSets = dataSets[::-1]
# dataSets = dataSets[14::]
dataSets = dataSets[::20]
if box == 'NGC Cu':
# dataSets = dataSets[20:341]
dataSets = dataSets[365::]
dataSets = dataSets[::-1]
dataSets = dataSets[17::]
dataSets = dataSets[::20]
if box == 'NGC Cu2':
# dataSets = dataSets[dataSet+1:133]
# dataSets = dataSets[::4]
dataSets = [dataSets[4]]
# dataSets = dataSets[0:1]
# del dataSets[3]
cm = plt.cm.get_cmap('RdYlBu')
for dataSet in dataSets:
#
# Import Data Set: Time, fluxV, I and Q
#
data = d[dataSet]
color = np.float(string.split(data.parameters.get('stepAmp'))[0])
print 'color: ',color
if color > 0.28:
decimation = 1
times = data.T[0]
fluxV = data.T[1]
flux = (fluxV * fluxVScale + fluxVOffset - np.pi/2)/np.pi
I = data.T[2]
Q = data.T[3]
#
# Shift I and Q data back in time to align step edge
#
shift = 38.9e-9 # sec
shiftPts = np.int(shift/(((times.max()-times.min())/len(times))*10e-10))
print 'shiftPts: ',shiftPts
times = np.array(times[:-shiftPts])
flux = np.array(flux[:-shiftPts])
fluxV = np.array(fluxV[:-shiftPts])
I = np.array(I[shiftPts:])
Q = np.array(Q[shiftPts:])
#
# Rotate I and Q by fit params (arbitrary theta rotation) back to 0.
#
Iscaled = aI * (I * np.cos(-thetaI) - Q * np.sin(-thetaI))
Qscaled = aQ * (I * np.sin(-thetaQ) + Q * np.cos(-thetaQ))
# if box == 'NGC Cu' or box == 'NGC Cu2':
if box == 'NGC Cu':
thetaI = 1.5
thetaQ = 1.5
Iscaled = 1 * (I * np.cos(-thetaI) - Q * np.sin(-thetaI))
Qscaled = 1 * (I * np.sin(-thetaQ) + Q * np.cos(-thetaQ))
# Iscaled = I
# Qscaled = Q
#
# Compute and unwrap the phase
# reverse the phase for unwrapping since the ending phase should always be the same.
#
# I = I[::-1]
# Q = Q[::-1]
phases = np.arctan(Qscaled/Iscaled)
phases = (np.unwrap(phases[::-1]*2, discont = np.pi)/2)/np.pi
phases = phases[::-1]
#
# Compute Lj from phase and fit params
#
Lj = 1/(omega *((np.tan(phases)/(2*Z0)) + (omega * cap)))
#
# Compute phi/phi0 from Lj and fit params
#
fluxComputed = np.arccos((phi0/(Lj * 2 * np.pi * Ic)))/np.pi
#
# Decimate to plot
#
times = times [::decimation]
flux = flux[::decimation]
fluxV = fluxV[::decimation]
I = I[::decimation]
Q = Q[::decimation]
phases = phases[::decimation]
fluxComputed = fluxComputed[::decimation]
#
# Convert phase to
A1 = np.array(flux[100]).mean()
B1 = np.array(flux[-100:]).mean()
A2 = np.array(fluxComputed[100]).mean()
B2 = np.array(fluxComputed[-100]).mean()
fluxComputed = (fluxComputed - A2)/(B1-A1)
flux= (flux - A1)/(B1-A1)
# phase = np.arctan(Q/I)
cm = plt.cm.get_cmap('RdYlBu')
# sc = plt.scatter(times, phases, c = np.zeros(len(times)) + color, vmin = 0, vmax = 3.5, cmap = cm, edgecolors='none')
if max(np.abs(phases)) < 60 and (np.max(phases) - np.min(phases) > 0.75):
# sc = plt.scatter(times, fluxComputed, c = np.zeros(len(times)) + color, vmin = 0.3, vmax = 3.5, cmap = cm, edgecolors='none', alpha = 0.5, label = '%s V'%color)
# sc = plt.scatter(times, flux, c = np.zeros(len(times)) + color, vmin = 0.3, vmax = 3.5, cmap = cm, edgecolors='none', alpha = 0.5, label = '%s V'%color)
plt.plot(times, fluxComputed, 'rs--', label = 'response', markeredgecolor='none')
plt.plot(times, flux, 'bo--', label = 'input', markeredgecolor='none')
# plt.plot(times, fluxComputed/flux, 'cs--', label = 'input', markeredgecolor='none')
# plt.plot(times, phases, alpha = 0.5, c = color, cmap = cm)
# Lj = 2 * Z0 / (np.tan(phases%np.pi) - 2 * omega * cap * Z0)
# computedFlux = (1/np.pi) * np.arccos(phi0 / (2 * np.pi * Ic * Lj))
# sc = plt.scatter(times, computedFlux, c = np.zeros(len(times)) + color, vmin = 0, vmax = 3.5, cmap = cm, edgecolors='none')
# plt.colorbar(sc, label = 'Step Voltage (Volts)')
# plt.title('Output Phase/Input Voltage vs. Time (nsec)',fontsize=20)
# plt.ylabel('Arb (not even linear)')
plt.title('Flux (Phi0) vs. Time (nsec)',fontsize=20)
plt.ylabel('Flux (Phi0)')
plt.xlabel('Time (nsec)')
plt.legend(numpoints =1,loc = 1)
# plt.axis([-10, 100, -0.2,0.2 ])
plt.show()
def DCIQcofit(sample, dataSet = None, plot = False, bound = 0.15, dataDir = None, useReg = False):
cxn = sample._cxn
dvw.cache.clear()
if dataDir == None:
d = dvw(sample['IQcalibrationDir'], cxn)
else:
d = dvw(dataDir,cxn)
if dataSet == None:
dataSet = sample['IQcalibrationDataSet']
#
# Import Data Set: fluxV, I and Q
#
data = d[dataSet]
I = data['I']
Q = data['Q']
fluxV = data['FluxV']
# convert flux voltage from DMM volts to DAC amp using the scaling from the datavault
if useReg:
paramSource = sample
else:
paramSource = data
#convert flux voltage from DMM volts to DAC amp
fluxV = dmmVtoDACamp(fluxV * V, paramSource, useReg = useReg)
phase = IQtoPhase(I, Q)
# create interp with linear spacing in flux.
phaseToVolts = sc.interpolate.interp1d(phase, fluxV)
voltsToPhase = sc.interpolate.interp1d(fluxV, phase)
linFluxV = np.linspace(np.min(fluxV), np.max(fluxV), 501)
linPhase = voltsToPhase(linFluxV)
#
# Function to fit the fluxV per Phi0 period
#
def periodFit(fluxV, Amplitude, Offset, Phase, Period):
return Amplitude * np.abs(np.cos(fluxV * Period + Phase)) + Offset
# p0s = [Amplitude, Offset, Phase, Period]
p0s = [1.3,-1.0,0.1 * np.pi ,2*np.pi/0.7]
poptA, pcovA = curve_fit(periodFit, linFluxV, linPhase, p0 = p0s, maxfev = 5200)
p0s = [1.3,-1.0,0.5 * np.pi ,2*np.pi/0.7]
poptB, pcovB = curve_fit(periodFit, linFluxV, linPhase, p0 = p0s, maxfev = 5200)
perrA = np.linalg.norm(np.sqrt(pcovA[2][2]**2 + pcovA[3][3]**2))
perrB = np.linalg.norm(np.sqrt(pcovB[2][2]**2 + pcovB[3][3]**2))
if perrA < perrB:
popt = poptA
pcov = pcovA
else:
popt = poptB
pcov = pcovB
dacAmpToFluxGain = popt[3]/np.pi
dacAmpToFluxOffset = popt[2]/np.pi
sample['dacAmpToFluxGain'] = dacAmpToFluxGain
sample['dacAmpToFluxOffset'] = dacAmpToFluxOffset
print 'Volts per cycle: ', (2.0/dacAmpToFluxGain)
print 'Volts shift: ', dacAmpToFluxOffset
#
# plot fit on data.
#
xvals = np.linspace(np.min(fluxV), np.max(fluxV), 601)
yvals = periodFit(xvals, popt[0], popt[1], popt[2], popt[3])
if plot:
plt.figure()
plt.plot(xvals, yvals, 'b--', label = 'fit')
plt.plot(linFluxV, linPhase, 'rs', label = 'data')
plt.title('Make sure fit period and phase match data',fontsize=20)
plt.ylabel('phase (arb)')
plt.xlabel('Flux Voltage (Volts)')
plt.legend(numpoints =1,loc = 1)
plt.show()
flux = dacAmpToFlux(linFluxV, paramSource, useReg = useReg)
sample['minFlux'] = np.min(flux)
sample['maxFlux'] = np.max(flux)
if plot:
plt.figure()
plt.plot(flux, linPhase, 'rs', label = 'Phase')
plt.title('Make sure Phase centered at 0 flux and zero at phi0/2',fontsize=20)
plt.ylabel('Phase (rad)')
plt.xlabel('Flux (phi0)')
plt.show()
def step_response(sample, startFlux = 0.0, endFlux = 0.39, averages = 50, plot = False, sampleRate = 5e8, horizScale = 203e-06):
cxn = sample._cxn
dvw.cache.clear()
print 'DIR: ', sample['IQcalibrationDir']
print 'DataSet: ', sample['IQcalibrationDataSet']
d = dvw(sample['IQcalibrationDir'], cxn)
dataSet = sample['IQcalibrationDataSet']
calData = d[dataSet]
Ical = calData['I']
Qcal = calData['Q']
# get FluxV in DMM volts and scale to DACamp
fluxV = calData['FluxV']
fluxV = dmmVtoDACamp(fluxV*V, calData, useReg = False)
# compute phase and unwrap
phase = IQtoPhase(Ical, Qcal)
# generate interpolation functions from calibration
phaseToVolts = sc.interpolate.interp1d(phase, fluxV)
voltsToPhase = sc.interpolate.interp1d(fluxV, phase)
dacAmpToI = sc.interpolate.interp1d(fluxV, Ical)
dacAmpToQ = sc.interpolate.interp1d(fluxV, Qcal)
# Config Hittite to match settings from calibration
config_Hittite(sample,
loFreq = calData.parameters['loFreq'],
loPower = calData.parameters['loPower'],
outputState = 'ON')
sample['startFlux'] = startFlux
sample['endFlux'] = endFlux
# Compute start and end DAC amps to generate the step
startDACamp = fluxToDACamp(startFlux, calData, useReg = False)
endDACamp = fluxToDACamp(endFlux, calData, useReg = False)
sample['startDACamp'] = startDACamp
sample['endDACamp'] = endDACamp
# Compute start and end DMM voltages
startDMMvoltage = dacAmpToDMMV(startDACamp, calData, useReg = False)
endDMMvoltage = dacAmpToDMMV(endDACamp, calData, useReg = False)
sample['startDMMvoltage'] = startDMMvoltage
sample['endDMMvoltage'] = endDMMvoltage
# Compute start and end DMM voltages
startScopeVoltage = dacAmpToScopeV(startDACamp, calData, useReg = False)
endScopeVoltage = dacAmpToScopeV(endDACamp, calData, useReg = False)
sample['startScopeVoltage'] = startScopeVoltage
sample['endScopeVoltage'] = endScopeVoltage
# Compute start and end I values
startI = np.float(dacAmpToI(startDACamp))
endI = np.float(dacAmpToI(endDACamp))
print 'startI: ', type(startI), startI
sample['startI'] = startI
sample['endI'] = endI
# Compute start and end Q values
startQ = np.float(dacAmpToQ(startDACamp))
endQ = np.float(dacAmpToQ(endDACamp))
sample['startQ'] = startQ
sample['endQ'] = endQ
print ''
print ''
print 'start I voltage: %s , end I voltage: %s'%(startI, endI)
print 'start Q voltage: %s , end Q voltage: %s'%(startQ, endQ)
print 'start Flux: %s , end Flux: %s'%(startFlux, endFlux)
print 'start DAC voltage: %s , end DAC voltage: %s'%(startDACamp, endDACamp)
print 'start DMM voltage: %s , end DMM voltage: %s'%(startDMMvoltage, endDMMvoltage)
print 'start scope voltage: %s , end scope voltage: %s'%(startScopeVoltage, endScopeVoltage)
print ''
print ''
sample['scanType'] = 'step_response'
DAC_square(sample, lowAmp = startDACamp, highAmp = endDACamp)
xs = []
ys = []
zs = []
for (x,y,z) in zip(fluxV, Ical, Qcal):
if np.min([startDACamp, endDACamp]) < x and x < np.max([startDACamp, endDACamp]):
xs.append(x)
ys.append(y)
zs.append(z)
minI = np.min(ys)
maxI = np.max(ys)
print 'minI: %s ; maxI: %s'%(minI, maxI)
minQ = np.min(zs)
maxQ = np.max(zs)
print 'minQ: %s ; maxQ: %s'%(minQ, maxQ)
overRange = (3.0/8.0) #150% of range/ 8 divisions
# compute scales and y positions for scope channels
# scales = [np.abs(startScopeVoltage['V'] - endScopeVoltage['V'])*overRange, np.abs(minI-maxI)*overRange, np.abs(minQ-maxQ)*overRange, 0.5]
# positions = [np.mean([startScopeVoltage['V'],endScopeVoltage['V']]), np.mean([minI,maxI]), np.mean([minQ,maxQ]), 0.0]
trig_level = np.mean([startScopeVoltage['V'],endScopeVoltage['V']])+ 0.0 * np.abs(endScopeVoltage['V'] - startScopeVoltage['V'])
print 'Trigger at: ', trig_level
fluxVsub = np.array(xs)
phaseSub = voltsToPhase(xs)
Isub = dacAmpToI(xs)
Qsub = dacAmpToQ(xs)
if plot:
# plt.figure()
# plt.title('Calibration IQ points vs DMM Flux Voltage')
# plt.plot(dacAmpToFlux(fluxV, calData, useReg = False), phase, 'rs-', label = 'phase')
# plt.plot(dacAmpToFlux(fluxVsub, calData, useReg = False), phaseSub, 'bs', label = 'step phase')
# plt.xlabel('Flux (phi0)')
# plt.ylabel('DMM Volts')
# plt.legend(numpoints = 1, loc = 4)
# plt.show()
# plt.figure()
# plt.title('Calibration Phase vs DAC amp')
# plt.plot(fluxV, phase, 'rs-', label = 'phase')
# plt.plot(fluxVsub, phaseSub, 'bs', label = 'step phase')
# plt.xlabel('DAC amp')
# plt.ylabel('rad')
# plt.legend(numpoints = 1, loc = 4)
# plt.show()
plt.figure()
plt.title('Calibration IQ points vs DAC amp')
plt.plot(fluxV, Ical, 'r-', label = 'I')
plt.plot(fluxVsub, Isub, 'rs', label = 'Istep')
plt.plot(fluxV, Qcal, 'b-', label = 'Q')
plt.plot(fluxVsub, Qsub, 'bs', label = 'Qstep')
plt.plot(startDACamp, startI, 'rx', markersize = 10, linewidth = 10, label = 'start I')
plt.plot(startDACamp, startQ, 'bx', markersize = 10, linewidth = 10, label = 'start Q')
plt.plot(endDACamp, endI, 'rs', markersize = 10, linewidth = 10, label = 'end I')
plt.plot(endDACamp, endQ, 'bs', markersize = 10, linewidth = 10, label = 'end Q')
plt.xlabel('DAC amp')
plt.ylabel('Scope Volts')
plt.legend(numpoints = 1, loc = 4)
plt.show()
# plt.figure()
# plt.title('Calibration IQ points vs DMM Flux Voltage')
# plt.plot(fluxV, calData['I'], 'rs-', label = 'I')
# plt.plot(fluxV, calData['Q'], 'bs-', label = 'Q')
# plt.xlabel('Flux Voltage (DMM volts)')
# plt.ylabel('DMM Volts')
# plt.legend(numpoints = 1, loc = 4)
# plt.show()
stepResp = InfiniiumScan(sample, averages = averages, sampleRate = sampleRate,
horizScale = horizScale, config = True, record = True,
name = 'step_response',
trig_level = trig_level)
if plot:
checkStep(sample, -1)
# times = stepResp.T[0]
# inputVoltage = stepResp.T[1] * V
# respI = stepResp.T[2]
# respQ = stepResp.T[3]
# plt.figure()
# plt.title('I step vs expected')
# plt.plot(times, respI, 'rs', label = 'response I')
# plt.plot([times[0],times-1], [startScopeV, startScopeV], 'b-', linewidth = 5, label = 'Expected Start I')
# plt.plot([times[0],times-1], [endScopeV, endScopeV], 'g-', linewidth = 5, label = 'Expected end I')
# plt.legend(numpoints = 1, loc = 3)
# plt.show()
# # inputVoltage = scopeVtoDACamp(inputVoltage, calData, useReg = False)
# # inputPhase = voltsToPhase(inputVoltage)
# # # inputFlux = dacAmpToFlux(inputVoltage, calData, useReg = False)
# responsePhase = np.arctan(respQ/respI)
# responsePhase = ((np.unwrap(responsePhase[::-1]*2, discont = np.pi)/2)/np.pi)[::-1]
# # responseFluxV = phaseToVolts(responsePhase)
# # responseFlux = dacAmpToFlux(responsePhase)
# # plt.plot(times, inputPhase, 'r-', label = 'input Flux')
# plt.plot(times, responsePhase, 'b-', label = 'response Flux')
# plt.xlabel('times')
# plt.ylabel('flux (Phi0)')
# plt.legend(loc = 4, numpoints = 1)
# plt.show()
del sample['startFlux']
del sample['endFlux']
del sample['startDACamp']
del sample['endDACamp']
del sample['startDMMvoltage']
del sample['endDMMvoltage']
del sample['startScopeVoltage']
del sample['endScopeVoltage']
return stepResp
def summary(s, set_qubit=None, directory=None, plot=False, Brass=False,
falling=False, set_amps=None, frequencies=False):
dataDirsTFunc = []
longZdataSets = []
# Directory to look for settle times:
if directory is None:
dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'NGQ', '141020'])
else:
dataDirsTFunc.append(directory)
cxn = s._cxn
plt1 = plt.figure()
counter = 0
colors = ['r', 'b', 'g', 'c', 'm', 'y', 'k', 'r', 'b', 'g', 'c', 'm', 'y',
'k', 'r', 'b', 'g', 'c', 'm', 'y', 'k', 'r', 'b', 'g', 'c', 'm',
'y', 'k', 'r', 'b', 'g', 'c', 'm', 'y', 'k', 'r', 'b', 'g', 'c',
'm', 'y', 'k', 'r', 'b', 'g', 'c', 'm', 'y', 'k', 'r', 'b', 'g',
'c', 'm', 'y', 'k', 'r', 'b', 'g', 'c', 'm', 'y', 'k', 'r', 'b',
'g', 'c', 'm', 'y', 'k', 'r', 'b', 'g', 'c', 'm', 'y', 'k', 'r',
'b', 'g', 'c', 'm', 'y', 'k', 'r', 'b', 'g', 'c', 'm', 'y', 'k',
'r', 'b', 'g', 'c', 'm', 'y', 'k', 'r', 'b', 'g', 'c', 'm', 'y',
'k', 'r', 'b', 'g', 'c', 'm', 'y', 'k']
ordering = []
t0s = []
t1s = []
t2s = []
a0s = []
a1s = []
a2s = []
zamps = []
for dataDirTFunc in dataDirsTFunc:
d = dvw(dataDirTFunc, cxn)
# Pull out all data sets of this type in the directory
settleDataSets = (d[int(x[:5])] for x in d.keys() if
'Z Settle Time Long Rise' in x)
if falling:
settleDataSets = (d[int(x[:5])] for x in d.keys() if
'Spectroscopy Z settle falling' in x)
for data in settleDataSets:
biasvals = []
freqvals = []
freqvals2 = []
timevals = []
print 'dir: {}'.format(dataDirTFunc)
print 'DATA SET:', data.name
# get data set from data vault
qubit = data.parameters['measure'][0]
q = data.parameters[qubit]
# Get spec peaks
fit_gauss_max = lambda x, y: fitting.getMaxGauss(x, y,
fitToMax=True)
spectroscopylen = data.parameters[qubit]['spectroscopyLen']['ns']
times, freqs = fitting.minima_cuts(data, 0, method=fit_gauss_max, plot=False)
times += 50 + spectroscopylen/2.
z_amplitude = float(
data.parameters['stack']['frame0']['arg']['z_amplitude'])
conditions = True
if set_qubit is not None:
if qubit != set_qubit:
conditions = False
if set_amps is not None:
if float(
data.parameters['stack']['frame0']['arg']['z_amplitude']) not in set_amps:
conditions = False
print 'set_amp: {}; data_amp: {}'.format(set_amps, float(
data.parameters['stack']['frame0']['arg']['z_amplitude']))
if len(times) > 50 and conditions:
zamps.append(z_amplitude)
# get zfunc and inverse zfunc
Z = zfuncs.TransmonFrequency(q['zFuncAna'])
def fitSettle(ts, a0, t0, a1, t1, a2, t2, b):
outp = []
for t in ts:
# outp.append((a0 * np.exp(-t/t0)) + (a1 * np.exp(-t/t1)) + (a2 * np.exp(-t/t2)) + b)
# two Tau fit.
outp.append(
(a1 * np.exp(-t / t1)) + (a2 * np.exp(-t / t2)) + b)
return np.array(outp)
# seed estimates [A0, T0, A1, T1, A2, T2, B]
# A0 ~ 5 MHz
# A1 ~ 5 MHz
# A2 ~ 1 MHz
# T0 ~ 10 nsec
# T1 ~ 1000 nsec
# T2 ~ 50000 nsec
# B ~ a frequency.
popt1, pcov1 = curve_fit(fitSettle, times, freqs,
p0=[5, 10, 5, 1000, 1, 50000,
freqs[-1]], maxfev=5200)
f_step_amp = ((Z.amp_to_freq(0.0)['GHz']) * 1000) - popt1[6]
a0s.append(popt1[0] / f_step_amp)
a1s.append(popt1[2] / f_step_amp)
a2s.append(popt1[4] / f_step_amp)
t0s.append(popt1[1])
t1s.append(popt1[3])
t2s.append(popt1[5])
print 'a0: ', a0s[-1]
print 't0: ', t0s[-1]
print 'a1: ', a1s[-1]
print 't1: ', t1s[-1]
print 'a2: ', a2s[-1]
print 't2: ', t2s[-1]
print 'b: ', popt1[6]
ordering.append((popt1[1] < popt1[3] and popt1[3] < popt1[5]))
print 'correct ordeR???', ordering[-1]
infSettleZ = Z.freq_to_amp((popt1[6] / 1000) * GHz)
xs = np.array(times)
ys = np.array(freqs)
x_vals = np.linspace(np.min(times), np.max(times), 10000)
y_vals = fitSettle(x_vals, popt1[0], popt1[1], popt1[2],
popt1[3], popt1[4], popt1[5], popt1[6])
y_vals = Z.freq_to_amp((np.array(y_vals) / 1000) * GHz)
y_vals = y_vals / infSettleZ
decay_time = np.floor(popt1[2] / 100) / 10
shortAmp = ((popt1[0] / f_step_amp) * 100)
shortTime = np.floor(popt1[2] / 100) / 10
print 'Decay short amp: %s percent' % shortAmp
print 'Decay short time: ', shortTime, 'us'
longAmp = ((popt1[5] / f_step_amp) * 100)
longTime = np.floor(popt1[6] / 100) / 10
print 'Decay long amp: %s percent' % longAmp
print 'Decay long time: ', longTime, 'us'
print qubit
# Amplitude of step from DAC
z_amplitude = float(
data.parameters['stack']['frame0']['arg']['z_amplitude'])
print ' z_amplitude', z_amplitude
# Amplitude of step according to zfunc
zfunc_amplitudes = (Z.freq_to_amp((np.array(freqs) / (1000)) * GHz))
zfunc_amplitudes = zfunc_amplitudes / infSettleZ
counter = int(z_amplitude * 10)
# data
# plt.semilogx(times/1000,zfunc_amplitudes*100, color = colors[counter], lw = 0, marker = 's',markeredgecolor='none', label = labels[counter])
# plt.semilogx(times/1000,zfunc_amplitudes*100, color = colors[counter], lw = 0, marker = 's',markeredgecolor='none', label = 'z_step_amp: %0.1f ;Tshort %.5f us; Ashort: %0.2d; Tlong %02d us; Along: %0.2d'%(z_amplitude,float(shortTime), np.float(shortAmp), longTime, np.float(longAmp)))
f_step_amp = np.int(f_step_amp*10.)/10.
if frequencies:
plt.semilogx(times / 1000., freqs,
color=colors[counter], lw=0, marker='s',
label='<{}> {}: z_amplitude: {}; f_step_amp: {} MHz'.format(
np.int(data.name.split()[0]), qubit,
z_amplitude, f_step_amp))
plt.ylabel('Qubit Frequency (MHz)', fontsize=18)
else:
plt.semilogx(times / 1000., zfunc_amplitudes * 100,
color=colors[counter], lw=0, marker='s',
label='<{}> {}: z_amplitude: {}; f_step_amp: '
'{} MHz'.format(
np.int(data.name.split()[0]), qubit,
z_amplitude, f_step_amp))
plt.ylabel('Z Amp (% of step)', fontsize=18)
# plt.axis([0.03, 24000, 90, 102])
print type(z_amplitude), type(shortTime)
# fit
# plt.semilogx(x_vals/1e3,y_vals*100, color = colors[counter], lw = 2, marker = None, label = 'z_step_amp: %s ;Tshort %s us; Ashort: %s; Tlong %s us; Along: %s'%(str(z_amplitude),str(shortTime), str(shortAmp), str(longTime), str(longAmp)))
plt.semilogx(x_vals / 1000, y_vals * 100, color=colors[counter],
lw=2, marker='s', markeredgecolor='none')
counter += 1
# plt.title(dataDirTFunc, fontsize=18)
# plt.xlabel('Time (us)', fontsize=18)
# plt.tick_params(labelsize=15)
# plt.legend(numpoints=1, loc=4)
# plt.tight_layout
# plt.show()
if plot:
plt.title(dataDirTFunc, fontsize=18)
plt.xlabel('Time (us)', fontsize=18)
plt.tick_params(labelsize=15)
plt.legend(numpoints=1, loc=4)
plt.tight_layout()
plt.show()
if False:
plt.figure()
plt.title(
'Settle Amplitude vs Step Amplitude: a0*exp(-t/t0) + a1*exp(-t/t1) + a2*exp(-t/t2)',
fontsize=11)
plt.ylabel('Settle amplitude (% of step)')
plt.xlabel('Step amplitude (DAC units)')
plt.plot(zamps, a0s, 'rs', label='a0s')
plt.plot(zamps, a1s, 'bs', label='a1s')
plt.plot(zamps, a2s, 'gs', label='a2s')
plt.legend(numpoints=1, loc=2)
plt.axis([-1.0, 1.0, -0.5, 0.5])
plt.tight_layout()
plt.show()
plt.figure()
plt.title(
'Settle Amplitude vs Step Amplitude: a0*exp(-t/t0) + a1*exp(-t/t1) + a2*exp(-t/t2)',
fontsize=11)
plt.ylabel('Settle amplitude (% of step)')
plt.xlabel('Step amplitude (DAC units)')
plt.semilogy(zamps, np.abs(a0s), 'rs', label='a0s')
plt.semilogy(zamps, np.abs(a1s), 'bs', label='a1s')
plt.semilogy(zamps, np.abs(a2s), 'gs', label='a2s')
plt.legend(numpoints=1, loc=3)
# plt.axis([-1.0,1.0, 0, 10])
plt.tight_layout()
plt.show()
plt.figure()
plt.title(
'Settle Times vs Step Amplitude: a0*exp(-t/t0) + a1*exp(-t/t1) + a2*exp(-t/t2)',
fontsize=11)
plt.ylabel('Settle Time nsec')
plt.xlabel('Step amplitude (DAC units)')
plt.semilogy(zamps, t0s, 'rs', label='t0s')
plt.semilogy(zamps, t1s, 'bs', label='t1s')
plt.semilogy(zamps, t2s, 'gs', label='t2s')
plt.legend(numpoints=1, loc=2)
# plt.axis([-1.0,1.0, 0, 10])
plt.tight_layout()
plt.show()
return zamps, a0s, a1s, a2s, t0s, t1s, t2s
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AlBoxViasPCB', '160415'])
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AlBoxViasPCB', '160415'])
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AlBoxViasPCB', '160415'])
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AlBoxViasPCB', '160415'])
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AlBoxViasPCB', '160415'])
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AlBoxViasPCB', '160415'])
# longZdataSets.append(63)
# longZdataSets.append(64)
# longZdataSets.append(65)
# longZdataSets.append(66)
# longZdataSets.append(67)
# longZdataSets.append(68)
# longZdataSets.append(69)
# # title_plot = 'Al Box, Copper vias Q2'
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AlBoxAlCuPCB', '160305'])
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AlBoxAlCuPCB', '160305'])
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AlBoxAlCuPCB', '160305'])
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AlBoxAlCuPCB', '160305'])
# longZdataSets.append(77)
# longZdataSets.append(78)
# longZdataSets.append(79)
# longZdataSets.append(80)
# # machined Al box, Q3
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AndrewsChip', 'D1', '160304'])
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AndrewsChip', 'D1', '160304'])
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'AndrewsChip', 'D1', '160304'])
# longZdataSets.append(17)
# longZdataSets.append(13)
# longZdataSets.append(13)
# Brass box Cu Vias, Al PCB
# dataDirsTFunc.append(['', 'Brooks', 'qubitData', 'BrassBoxCuViasAlPCB', '160418'])
# # Amp = 0.4
# longZdataSets.append(90)
def go(sample, stepDataSet, stepDataDir = None, calDataSet = None, plot = False):
cxn = sample._cxn
# get calibration datasets
if stepDataDir == None:
d = dvw(sample, cxn)
else:
d = dvw(dataDir, cxn)
calDataSet = sample['IQcalibrationDataSet']
calDataDir = sample['IQcalibrationDir']
calD = dvw(calDataDir, cxn)
calData = calD[calDataSet]
fluxVcal = calData['FluxV']
Ical = calData['I']
Qcal = calData['Q']
# convert flux voltage from dmmV to DAC amp
fluxDACampCal = dmmVtoDACamp(fluxVcal * V, calData, useReg = False)
# convert DAC amp flux voltage to flux
fluxCal = dacAmpToFlux(fluxDACampCal, calData, useReg = False)
# compute Cal phase
phaseCal = IQtoPhase(Ical, Qcal)
stepData = d[stepDataSet]
fluxV = stepData['FluxV']
fluxVDAC = scopeVtoDACamp(fluxV * V, stepData, useReg = False)
times = stepData['Time']
flux = dacAmpToFlux(fluxVDAC, stepData, useReg = False)
I = stepData['I']
Q = stepData['Q']
# shift I back to Q
shift = 1.1 # sec
shiftPts = np.int(shift/(((times.max()-times.min())/len(times))))
print 'shiftPts: ',shiftPts
if shiftPts > 0:
times = np.array(times[:-shiftPts])
flux = np.array(flux[:-shiftPts])
Q = np.array(Q[:-shiftPts])
I = np.array(I[shiftPts:])
if plot:
# plot I&Q to time check shift
plt.figure()
plt.title('CHECK TIME SHIFT Response Signals I and Q')
plt.plot(times, I, 'r-',label = 'I')
plt.plot(times, Q, 'b-',label = 'Q')
plt.ylabel('Voltage (V)')
plt.xlabel('Time (ns)')
plt.legend(numpoints =1,loc = 3)
plt.tight_layout()
plt.show()
phase = IQtoPhase(I, Q)
phaseSub = []
fluxSub = []
for p,f in zip(phaseCal, fluxCal):
if f < np.max(flux) and f > np.min(flux):
phaseSub.append(p)
fluxSub.append(f)
phaseToFlux = sc.interpolate.interp1d(phaseSub, fluxSub)
fluxToPhase = sc.interpolate.interp1d(fluxCal, phaseCal)
interpFluxes = np.linspace(np.min(fluxSub), np.max(fluxSub), 1000)
interpPhases = fluxToPhase(interpFluxes)
# plot volts to phase interp func
if plot:
plt.figure()
plt.title('Phase response (rad) vs. Flux Voltage (Volts)')
plt.plot(fluxCal, phaseCal, 'rs',label = 'data')
plt.plot(interpFluxes, interpPhases, 'b-', label = 'interp')
plt.plot(flux[0], phase[0], 'rx', markersize = 20, label = 'start Flux')
plt.plot(flux[-1], phase[-1], 'bx', markersize = 20, label = 'end Flux')
plt.ylabel('phase (rad)')
plt.xlabel('Flux (phi0)')
plt.legend(numpoints =1,loc = 3)
plt.tight_layout()
plt.show()
interpPhases = np.linspace(np.min(phaseSub), np.max(phaseSub), 1000)
interpFluxes = phaseToFlux(interpPhases)
# plot phase to volts interp func
if plot:
plt.figure()
plt.title('Flux (phi0) vs. Phase response (rad)')
plt.plot(phaseCal, fluxCal, 'rs',label = 'data')
plt.plot(interpPhases, interpFluxes, 'b-', label = 'interp')
plt.xlabel('phase (rad)')
plt.ylabel('Flux (phi0)')
plt.legend(numpoints =1,loc = 3)
plt.tight_layout()
plt.show()
plt.figure()
plt.title('Response Phase (rad) vs Time (ns)')
plt.plot(times, phase, 'r-', linewidth = 10, label = 'phase')
plt.xlabel('Time (ns)')
plt.ylabel('phase (rad)')
plt.legend(numpoints = 1, loc = 3)
plt.tight_layout()
plt.show()
# shift Response phase back to stimulus flux
shift = 37.3 # sec
shiftPts = np.int(shift/(((times.max()-times.min())/len(times))))
print 'shiftPts: ',shiftPts
times = np.array(times[:-shiftPts])
flux = np.array(flux[:-shiftPts])
phase = np.array(phase[shiftPts:])
# plot input step & response phase to time check shift
if plot:
plt.figure()
plt.title('CHECK TIME SHIFT')
plt.plot(times, flux, 'r-',label = 'stimulus')
plt.plot(times, phase, 'b-',label = 'response')
plt.ylabel('NOT SAME Y SCALES')
plt.xlabel('Time (ns)')
plt.legend(numpoints =1,loc = 3)
plt.tight_layout()
plt.show()
def savitzky_golay(y, window_size, order, deriv=0, rate=1):
try:
window_size = np.abs(np.int(window_size))
order = np.abs(np.int(order))
except ValueError, msg:
raise ValueError("window_size and order have to be of type int")
if window_size % 2 != 1 or window_size < 1:
raise TypeError("window_size size must be a positive odd number")
if window_size < order + 2:
raise TypeError("window_size is too small for the polynomials order")
order_range = range(order+1)
half_window = (window_size -1) // 2
# precompute coefficients
b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])
m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)
# pad the signal at the extremes with
# values taken from the signal itself
firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )
lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])
y = np.concatenate((firstvals, y, lastvals))
return np.convolve( m[::-1], y, mode='valid')
