def analyze(self, all_plots: bool = False):
if self.t_arr is None:
raise RuntimeError
if self.store_arr is None:
raise RuntimeError
import matplotlib.pyplot as plt
ret_fig = []
idx = np.arange(IDX_LOW, IDX_HIGH)
t_low = self.t_arr[IDX_LOW]
t_high = self.t_arr[IDX_HIGH]
if all_plots:
# Plot raw store data for first iteration as a check
fig1, ax1 = plt.subplots(2, 1, sharex=True, tight_layout=True)
ax11, ax12 = ax1
ax11.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax12.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax11.plot(1e9 * self.t_arr, np.abs(self.store_arr[0, 0, :]))
ax12.plot(1e9 * self.t_arr, np.angle(self.store_arr[0, 0, :]))
ax12.set_xlabel("Time [ns]")
fig1.show()
ret_fig.append(fig1)
# Analyze T1
resp_arr = np.mean(self.store_arr[:, 0, idx], axis=-1)
resp_arr = rotate_opt(resp_arr)
# Fit data
popt, perr = _fit_simple(self.delay_arr, np.real(resp_arr))
T1 = popt[0]
T1_err = perr[0]
print("T1 time I: {} +- {} us".format(1e6 * T1, 1e6 * T1_err))
if all_plots:
fig2, ax2 = plt.subplots(4,
1,
sharex=True,
figsize=(6.4, 6.4),
tight_layout=True)
ax21, ax22, ax23, ax24 = ax2
ax21.plot(1e6 * self.delay_arr, np.abs(resp_arr))
ax22.plot(1e6 * self.delay_arr, np.unwrap(np.angle(resp_arr)))
ax23.plot(1e6 * self.delay_arr, np.real(resp_arr))
ax23.plot(1e6 * self.delay_arr, _decay(self.delay_arr, *popt),
'--')
ax24.plot(1e6 * self.delay_arr, np.imag(resp_arr))
ax21.set_ylabel("Amplitude [FS]")
ax22.set_ylabel("Phase [rad]")
ax23.set_ylabel("I [FS]")
ax24.set_ylabel("Q [FS]")
ax2[-1].set_xlabel("Control-readout delay [μs]")
fig2.show()
ret_fig.append(fig2)
# bigger plot just for I quadrature
data_max = np.abs(resp_arr.real).max()
unit = ""
mult = 1.0
if data_max < 1e-6:
unit = "n"
mult = 1e9
elif data_max < 1e-3:
unit = "μ"
mult = 1e6
elif data_max < 1e0:
unit = "m"
mult = 1e3
fig3, ax3 = plt.subplots(tight_layout=True)
ax3.plot(1e6 * self.delay_arr, mult * np.real(resp_arr), '.')
ax3.plot(1e6 * self.delay_arr, mult * _decay(self.delay_arr, *popt),
'--')
ax3.set_ylabel(f"I quadrature [{unit:s}FS]")
ax3.set_xlabel(r"Control-readout delay [μs]")
ax3.set_title("T1 = {:s} μs".format(
format_precision(1e6 * T1, 1e6 * T1_err)))
fig3.show()
ret_fig.append(fig3)
return ret_fig
def load(load_filename):
with h5py.File(load_filename, "r") as h5f:
num_averages = h5f.attrs["num_averages"]
control_freq = h5f.attrs["control_freq"]
readout_freq = h5f.attrs["readout_freq"]
readout_duration = h5f.attrs["readout_duration"]
readout_amp = h5f.attrs["readout_amp"]
control_amp = h5f.attrs["control_amp"]
sample_duration = h5f.attrs["sample_duration"]
rabi_n = h5f.attrs["rabi_n"]
rabi_dt = h5f.attrs["rabi_dt"]
wait_delay = h5f.attrs["wait_delay"]
readout_sample_delay = h5f.attrs["readout_sample_delay"]
t_arr = h5f["t_arr"][()]
store_arr = h5f["store_arr"][()]
source_code = h5f["source_code"][()]
t_low = 1500 * 1e-9
t_high = 2000 * 1e-9
t_span = t_high - t_low
idx_low = np.argmin(np.abs(t_arr - t_low))
idx_high = np.argmin(np.abs(t_arr - t_high))
idx = np.arange(idx_low, idx_high)
nr_samples = len(idx)
dt = t_arr[1] - t_arr[0]
f_arr = np.fft.rfftfreq(len(t_arr[idx]), dt)
fig1, ax1 = plt.subplots(2, 1, sharex=True, tight_layout=True)
ax11, ax12 = ax1
ax11.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax12.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax11.plot(1e9 * t_arr, np.abs(store_arr[0, 0, :]))
ax12.plot(1e9 * t_arr, np.angle(store_arr[0, 0, :]))
ax12.set_xlabel("Time [ns]")
fig1.show()
# Analyze Rabi
resp_arr = np.mean(store_arr[:, 0, idx], axis=-1)
data = rotate_opt(resp_arr)
len_arr = rabi_dt * np.arange(rabi_n)
# Fit data
# popt_a, perr_a = fit_period(len_arr, np.abs(data))
# popt_p, perr_p = fit_period(len_arr, np.angle(data))
popt_x, perr_x = fit_period(len_arr, np.real(data))
# popt_y, perr_y = fit_period(len_arr, np.imag(data))
period = popt_x[3]
period_err = perr_x[3]
pi_len = round(period / 2 / 2e-9) * 2e-9
pi_2_len = round(period / 4 / 2e-9) * 2e-9
print("Rabi period: {} +- {} ns".format(1e9 * period, 1e9 * period_err))
print("Pi pulse length: {:.0f} ns".format(1e9 * pi_len))
print("Pi/2 pulse length: {:.0f} ns".format(1e9 * pi_2_len))
print(f"decay: {popt_x[2]} s")
fig2, ax2 = plt.subplots(4, 1, sharex=True, figsize=(6.4, 6.4), tight_layout=True)
ax21, ax22, ax23, ax24 = ax2
ax21.plot(1e9 * len_arr, np.abs(data))
# ax21.plot(1e9 * len_arr, func(len_arr, *popt_a), '--')
ax22.plot(1e9 * len_arr, np.angle(data))
# ax22.plot(1e9 * len_arr, func(len_arr, *popt_p), '--')
ax23.plot(1e9 * len_arr, np.real(data))
ax23.plot(1e9 * len_arr, func(len_arr, *popt_x), '--')
ax24.plot(1e9 * len_arr, np.imag(data))
# ax24.plot(1e9 * len_arr, func(len_arr, *popt_y), '--')
ax21.set_ylabel("Amplitude [FS]")
ax22.set_ylabel("Phase [rad]")
ax23.set_ylabel("I [FS]")
ax24.set_ylabel("Q [FS]")
ax2[-1].set_xlabel("Pulse length [ns]")
fig2.show()
mult, unit = scale_unit(data.real)
mult_t, unit_t = scale_unit(len_arr)
fig3, ax3 = plt.subplots(tight_layout=True)
ax3.plot(mult_t * len_arr, mult * np.real(data), '.')
ax3.plot(mult_t * len_arr, mult * func(len_arr, *popt_x), '--')
ax3.set_xlabel(f"Pulse length [{unit_t:s}s]")
ax3.set_ylabel(f"I quadrature [{unit:s}FS]")
fig3.show()
return fig1, fig2, fig3
def analyze(self, crazy=False, blit=True):
if self.t_arr is None:
raise RuntimeError
if self.store_arr is None:
raise RuntimeError
import matplotlib.pyplot as plt
from presto.utils import rotate_opt
ret_fig = []
t_low = 1500 * 1e-9
t_high = 2000 * 1e-9
# t_span = t_high - t_low
idx_low = np.argmin(np.abs(self.t_arr - t_low))
idx_high = np.argmin(np.abs(self.t_arr - t_high))
idx = np.arange(idx_low, idx_high)
# nr_samples = len(idx)
# Analyze
resp_arr = np.mean(self.store_arr[:, 0, idx], axis=-1)
data = rotate_opt(resp_arr).real
nr_delays = len(self.ramsey_delay_arr)
nr_amps = len(self.first_pulse_amp_arr)
data.shape = (nr_amps, nr_delays)
self._amp_idx = 0
data_max = np.abs(data).max()
unit = ""
mult = 1.0
if data_max < 1e-6:
unit = "n"
mult = 1e9
elif data_max < 1e-3:
unit = "μ"
mult = 1e6
elif data_max < 1e0:
unit = "m"
mult = 1e3
if np.mean(data) < 0:
data *= -mult
else:
data *= mult
data_max = np.max(data)
data_min = np.min(data)
data_rng = data_max - data_min
# choose limits for colorbar
cutoff = 0.0 # %
lowlim = np.percentile(data, cutoff)
highlim = np.percentile(data, 100. - cutoff)
x_data = 1e9 * self.ramsey_delay_arr
y_data = self.first_pulse_amp_arr**2 * 100
# extent
x_min = x_data[0]
x_max = x_data[-1]
x_rng = x_max - x_min
dx = x_data[1] - x_data[0]
y_min = y_data[0]
y_max = y_data[-1]
dy = y_data[1] - y_data[0]
fig1 = plt.figure(tight_layout=True, figsize=(12.8, 4.8))
ax11 = fig1.add_subplot(1, 2, 1)
im = ax11.imshow(
data,
origin='lower',
aspect='auto',
interpolation='none',
extent=(x_min - dx / 2, x_max + dx / 2, y_min - dy / 2, y_max + dy / 2),
vmin=lowlim,
vmax=highlim,
)
self._line_sel = ax11.axhline(y_data[self._amp_idx], ls="--", c="k", lw=3, animated=blit)
ax11.set_xlabel("Ramsey delay [ns]")
ax11.set_ylabel("First pulse power [%]")
cb = fig1.colorbar(im)
# cb.set_label(f"Response I quadrature [{unit:s}FS]")
ax11.set_title(f"Response I quadrature [{unit:s}FS]")
ax12 = fig1.add_subplot(1, 2, 2)
ax12.yaxis.set_label_position("right")
ax12.yaxis.tick_right()
self._line_slice, = ax12.plot(x_data, data[self._amp_idx], '.', label="measured", animated=blit)
try:
if crazy:
popt, _ = _fit_simple(x_data, data[self._amp_idx])
fit_data = _func(x_data, *popt)
else:
popt, _ = _fit_simple(x_data, data[self._amp_idx])
fit_data = _func(x_data, *popt)
self._line_fit, = ax12.plot(x_data, fit_data, '--', label="fit", animated=blit)
except Exception:
self._line_fit, = ax12.plot(x_data, np.full_like(x_data, np.nan), '--', label="fit", animated=blit)
ax12.set_xlim(x_min - 0.05 * x_rng, x_max + 0.05 * x_rng)
ax12.set_ylim(data_min - 0.05 * data_rng, data_max + 0.05 * data_rng)
ax12.set_xlabel("Ramsey delay [ns]")
ax12.set_title(f"Response I quadrature [{unit:s}FS]")
ax12.legend(loc="lower right", ncol=2)
def onbuttonpress(event):
if event.inaxes == ax11:
self._amp_idx = np.argmin(np.abs(y_data - event.ydata))
update()
def onkeypress(event):
if event.inaxes == ax11:
if event.key == "up":
self._amp_idx += 1
if self._amp_idx >= nr_amps:
self._amp_idx = nr_amps - 1
update()
elif event.key == "down":
self._amp_idx -= 1
if self._amp_idx < 0:
self._amp_idx = 0
update()
def update():
self._line_sel.set_ydata([y_data[self._amp_idx], y_data[self._amp_idx]])
# ax1.set_title(f"amp = {amp_arr[AMP_IDX]:.2e}")
# print(f"drive amp {self._amp_idx:d}: {qubit_amp_arr[self._amp_idx]:.2e} FS = {amp_dBFS[self._amp_idx]:.1f} dBFS")
self._line_slice.set_ydata(data[self._amp_idx])
try:
if crazy:
popt, _ = _fit_simple(x_data, data[self._amp_idx])
fit_data = _func(x_data, *popt)
else:
popt, _ = _fit_simple(x_data, data[self._amp_idx])
fit_data = _func(x_data, *popt)
self._line_fit.set_ydata(fit_data)
except Exception:
self._line_fit.set_ydata(np.full_like(x_data, np.nan))
# ax2.set_title("")
if blit:
fig1.canvas.restore_region(self._bg)
ax11.draw_artist(self._line_sel)
ax12.draw_artist(self._line_slice)
ax12.draw_artist(self._line_fit)
fig1.canvas.blit(fig1.bbox)
# fig1.canvas.flush_events()
else:
fig1.canvas.draw()
fig1.canvas.mpl_connect('button_press_event', onbuttonpress)
fig1.canvas.mpl_connect('key_press_event', onkeypress)
fig1.show()
if blit:
fig1.canvas.draw()
# fig1.canvas.flush_events()
self._bg = fig1.canvas.copy_from_bbox(fig1.bbox)
ax11.draw_artist(self._line_sel)
ax12.draw_artist(self._line_slice)
ax12.draw_artist(self._line_fit)
fig1.canvas.blit(fig1.bbox)
ret_fig.append(fig1)
# Fit the lowest powers
idx_fit = np.s_[0:75]
pwr_fit = y_data[idx_fit]
n_fit = np.zeros_like(pwr_fit)
for ii, fit_data in enumerate(data[idx_fit]):
try:
popt, _ = _fit_simple(x_data, fit_data)
n_fit[ii] = popt[0]
except Exception:
n_fit[ii] = np.nan
fig2, ax2 = plt.subplots(tight_layout=True)
ax2.plot(pwr_fit, n_fit, '.')
ax2.grid()
ax2.set_xlabel("First pulse power [%]")
ax2.set_ylabel("Fitted number of photons")
fig2.show()
ret_fig.append(fig2)
return ret_fig
def load(load_filename):
with h5py.File(load_filename, "r") as h5f:
num_averages = h5f.attrs["num_averages"]
control_freq_1 = h5f.attrs["control_freq_1"]
control_freq_2 = h5f.attrs["control_freq_2"]
control_if = h5f.attrs["control_if"]
readout_freq_1 = h5f.attrs["readout_freq_1"]
readout_freq_2 = h5f.attrs["readout_freq_2"]
readout_duration = h5f.attrs["readout_duration"]
control_duration = h5f.attrs["control_duration"]
readout_amp = h5f.attrs["readout_amp"]
control_amp_1 = h5f.attrs["control_amp_1"]
control_amp_2 = h5f.attrs["control_amp_2"]
sample_duration = h5f.attrs["sample_duration"]
wait_delay = h5f.attrs["wait_delay"]
readout_sample_delay = h5f.attrs["readout_sample_delay"]
coupler_dc_bias = h5f.attrs["coupler_dc_bias"]
coupler_ac_amp = h5f.attrs["coupler_ac_amp"]
nr_freqs = h5f.attrs["nr_freqs"]
coupler_ac_duration = h5f.attrs["coupler_ac_duration"]
t_arr = h5f["t_arr"][()]
store_arr = h5f["store_arr"][()]
coupler_ac_freq_arr = h5f["coupler_ac_freq_arr"][()]
t_low = 1500 * 1e-9
t_high = 2000 * 1e-9
idx_low = np.argmin(np.abs(t_arr - t_low))
idx_high = np.argmin(np.abs(t_arr - t_high))
idx = np.arange(idx_low, idx_high)
# Plot raw store data for first iteration as a check
fig1, ax1 = plt.subplots(2, 1, sharex=True, tight_layout=True)
ax11, ax12 = ax1
ax11.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax12.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax11.plot(1e9 * t_arr, np.abs(store_arr[0, 0, :]))
ax12.plot(1e9 * t_arr, np.angle(store_arr[0, 0, :]))
ax12.set_xlabel("Time [ns]")
fig1.show()
# Analyze T2
resp_arr = np.mean(store_arr[:, 0, idx], axis=-1)
data = rotate_opt(resp_arr)
fig2, ax2 = plt.subplots(4,
1,
sharex=True,
figsize=(6.4, 6.4),
tight_layout=True)
ax21, ax22, ax23, ax24 = ax2
ax21.plot(coupler_ac_freq_arr, np.abs(data))
ax22.plot(coupler_ac_freq_arr, np.unwrap(np.angle(data)))
ax23.plot(coupler_ac_freq_arr, np.real(data))
ax24.plot(coupler_ac_freq_arr, np.imag(data))
ax21.set_ylabel("Amplitude [FS]")
ax22.set_ylabel("Phase [rad]")
ax23.set_ylabel("I [FS]")
ax24.set_ylabel("Q [FS]")
ax2[-1].set_xlabel("Coupler AC amplitude [FS]")
fig2.show()
return fig1, fig2
def analyze(self, all_plots: bool = False):
if self.t_arr is None:
raise RuntimeError
if self.store_arr is None:
raise RuntimeError
import matplotlib.pyplot as plt
ret_fig = []
idx = np.arange(IDX_LOW, IDX_HIGH)
t_low = self.t_arr[IDX_LOW]
t_high = self.t_arr[IDX_HIGH]
if all_plots:
# Plot raw store data for first iteration as a check
fig1, ax1 = plt.subplots(2, 1, sharex=True, tight_layout=True)
ax11, ax12 = ax1
ax11.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax12.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax11.plot(1e9 * self.t_arr, np.abs(self.store_arr[0, 0, :]))
ax12.plot(1e9 * self.t_arr, np.angle(self.store_arr[0, 0, :]))
ax12.set_xlabel("Time [ns]")
fig1.show()
ret_fig.append(fig1)
# Analyze Rabi
resp_arr = np.mean(self.store_arr[:, 0, idx], axis=-1)
data = rotate_opt(resp_arr)
# Fit data
popt_x, perr_x = _fit_period(self.control_amp_arr, np.real(data))
period = popt_x[3]
period_err = perr_x[3]
pi_amp = period / 2
pi_2_amp = period / 4
if self.num_pulses > 1:
print(f"{self.num_pulses} pulses")
print("Tau pulse amplitude: {} +- {} FS".format(period * self.num_pulses, period_err * self.num_pulses))
print("Pi pulse amplitude: {} +- {} FS".format(pi_amp * self.num_pulses, period_err / 2 * self.num_pulses))
print("Pi/2 pulse amplitude: {} +- {} FS".format(pi_2_amp * self.num_pulses, period_err / 4 * self.num_pulses))
if all_plots:
fig2, ax2 = plt.subplots(4, 1, sharex=True, figsize=(6.4, 6.4), tight_layout=True)
ax21, ax22, ax23, ax24 = ax2
ax21.plot(self.control_amp_arr, np.abs(data))
ax22.plot(self.control_amp_arr, np.angle(data))
ax23.plot(self.control_amp_arr, np.real(data))
ax23.plot(self.control_amp_arr, _func(self.control_amp_arr, *popt_x), '--')
ax24.plot(self.control_amp_arr, np.imag(data))
ax21.set_ylabel("Amplitude [FS]")
ax22.set_ylabel("Phase [rad]")
ax23.set_ylabel("I [FS]")
ax24.set_ylabel("Q [FS]")
ax2[-1].set_xlabel("Pulse amplitude [FS]")
fig2.show()
ret_fig.append(fig2)
data_max = np.abs(data).max()
unit = ""
mult = 1.0
if data_max < 1e-6:
unit = "n"
mult = 1e9
elif data_max < 1e-3:
unit = "μ"
mult = 1e6
elif data_max < 1e0:
unit = "m"
mult = 1e3
fig3, ax3 = plt.subplots(tight_layout=True)
ax3.plot(self.control_amp_arr, mult * np.real(data), '.')
ax3.plot(self.control_amp_arr, mult * _func(self.control_amp_arr, *popt_x), '--')
ax3.set_ylabel(f"I quadrature [{unit:s}FS]")
ax3.set_xlabel("Pulse amplitude [FS]")
if self.num_pulses > 1:
ax3.set_title(f"{self.num_pulses} pulses")
fig3.show()
ret_fig.append(fig3)
return ret_fig
def load(load_filename):
with h5py.File(load_filename, "r") as h5f:
num_averages = h5f.attrs["num_averages"]
control_freq_1 = h5f.attrs["control_freq_1"]
control_freq_2 = h5f.attrs["control_freq_2"]
control_if = h5f.attrs["control_if"]
readout_freq_1 = h5f.attrs["readout_freq_1"]
readout_freq_2 = h5f.attrs["readout_freq_2"]
readout_duration = h5f.attrs["readout_duration"]
control_duration = h5f.attrs["control_duration"]
readout_amp = h5f.attrs["readout_amp"]
control_amp_1 = h5f.attrs["control_amp_1"]
control_amp_2 = h5f.attrs["control_amp_2"]
sample_duration = h5f.attrs["sample_duration"]
wait_delay = h5f.attrs["wait_delay"]
readout_sample_delay = h5f.attrs["readout_sample_delay"]
coupler_dc_bias = h5f.attrs["coupler_dc_bias"]
nr_freqs = h5f.attrs["nr_freqs"]
nr_steps = h5f.attrs["nr_steps"]
dt_steps = h5f.attrs["dt_steps"]
coupler_ac_amp = h5f.attrs["coupler_ac_amp"]
t_arr = h5f["t_arr"][()]
store_arr = h5f["store_arr"][()]
coupler_ac_freq_arr = h5f["coupler_ac_freq_arr"][()]
coupler_ac_duration_arr = h5f["coupler_ac_duration_arr"][()]
# these were added later
try:
# multiplexed readout
readout_if_1 = h5f.attrs["readout_if_1"]
readout_if_2 = h5f.attrs["readout_if_2"]
readout_nco = h5f.attrs["readout_nco"]
except KeyError:
# only readout resonator 1
readout_if_1 = 0.0
readout_if_2 = None
readout_nco = readout_freq_1
t_low = 1500 * 1e-9
t_high = 2000 * 1e-9
idx_low = np.argmin(np.abs(t_arr - t_low))
idx_high = np.argmin(np.abs(t_arr - t_high))
idx = np.arange(idx_low, idx_high)
# Plot raw store data for first iteration as a check
# fig1, ax1 = plt.subplots(2, 1, sharex=True, tight_layout=True)
# ax11, ax12 = ax1
# ax11.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
# ax12.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
# ax11.plot(1e9 * t_arr, np.abs(store_arr[0, 0, :]))
# ax12.plot(1e9 * t_arr, np.angle(store_arr[0, 0, :]))
# ax12.set_xlabel("Time [ns]")
# fig1.show()
if readout_if_2 is None:
# only readout resonator 1
resp_arr = np.mean(store_arr[:, 0, idx], axis=-1)
data = rotate_opt(resp_arr)
data.shape = (nr_freqs, nr_steps)
plot_data = data.real
# choose limits for colorbar
cutoff = 0.0 # %
lowlim = np.percentile(plot_data, cutoff)
highlim = np.percentile(plot_data, 100. - cutoff)
# extent
x_min = 1e9 * coupler_ac_duration_arr[0]
x_max = 1e9 * coupler_ac_duration_arr[-1]
dx = 1e9 * (coupler_ac_duration_arr[1] - coupler_ac_duration_arr[0])
y_min = 1e-6 * coupler_ac_freq_arr[0]
y_max = 1e-6 * coupler_ac_freq_arr[-1]
dy = 1e-6 * (coupler_ac_freq_arr[1] - coupler_ac_freq_arr[0])
fig2, ax2 = plt.subplots(tight_layout=True)
im = ax2.imshow(
plot_data,
origin='lower',
aspect='auto',
interpolation='none',
extent=(x_min - dx / 2, x_max + dx / 2, y_min - dy / 2,
y_max + dy / 2),
vmin=lowlim,
vmax=highlim,
)
ax2.set_xlabel("Coupler duration [ns]")
ax2.set_ylabel("Coupler frequency [MHz]")
cb = fig2.colorbar(im)
cb.set_label("Response I quadrature [FS]")
fig2.show()
else:
# multiplexed readout
dt = t_arr[1] - t_arr[0]
nr_samples = len(idx)
freq_arr = np.fft.fftfreq(nr_samples, dt)
# complex FFT should take care of upper/lower sideband
resp_fft = np.fft.fft(store_arr[:, 0, idx], axis=-1) / len(idx)
idx_1 = np.argmin(np.abs(freq_arr - readout_if_1))
idx_2 = np.argmin(np.abs(freq_arr - readout_if_2))
resp_arr_1 = 2 * resp_fft[:, idx_1]
resp_arr_2 = 2 * resp_fft[:, idx_2]
data_1 = rotate_opt(resp_arr_1)
data_2 = rotate_opt(resp_arr_2)
data_1.shape = (nr_freqs, nr_steps)
data_2.shape = (nr_freqs, nr_steps)
plot_data_1 = data_1.real
plot_data_2 = data_2.real
# convert to population
# g_state = -0.000199191400873993
# e_state = -0.001772512103413742
# plot_data_1 = (data_1.real - g_state) / (e_state - g_state)
# g_state = -0.0005096104775241157
# e_state = 0.0010518469556880861
# plot_data_2 = (data_2.real - g_state) / (e_state - g_state)
if abs(np.min(plot_data_1)) > abs(np.max(plot_data_1)):
plot_data_1 *= -1
if abs(np.min(plot_data_2)) > abs(np.max(plot_data_2)):
plot_data_2 *= -1
# choose limits for colorbar
cutoff = 0.1 # %
lowlim_1 = np.percentile(plot_data_1, cutoff)
highlim_1 = np.percentile(plot_data_1, 100. - cutoff)
lowlim_2 = np.percentile(plot_data_2, cutoff)
highlim_2 = np.percentile(plot_data_2, 100. - cutoff)
# alldata = (np.r_[plot_data_1, plot_data_2]).ravel()
# lowlim = np.percentile(alldata, cutoff)
# highlim = np.percentile(alldata, 100. - cutoff)
# lowlim_1, highlim_1 = lowlim, highlim
# lowlim_2, highlim_2 = lowlim, highlim
# extent
x_min = 1e9 * coupler_ac_duration_arr[0]
x_max = 1e9 * coupler_ac_duration_arr[-1]
dx = 1e9 * (coupler_ac_duration_arr[1] - coupler_ac_duration_arr[0])
y_min = 1e-6 * coupler_ac_freq_arr[0]
y_max = 1e-6 * coupler_ac_freq_arr[-1]
dy = 1e-6 * (coupler_ac_freq_arr[1] - coupler_ac_freq_arr[0])
fig2, ax2 = plt.subplots(1, 2, figsize=(9.6, 4.8), tight_layout=True)
ax21, ax22 = ax2
im1 = ax21.imshow(
plot_data_1,
origin='lower',
aspect='auto',
interpolation='none',
extent=(x_min - dx / 2, x_max + dx / 2, y_min - dy / 2,
y_max + dy / 2),
vmin=lowlim_1,
vmax=highlim_1,
)
im2 = ax22.imshow(
plot_data_2,
origin='lower',
aspect='auto',
interpolation='none',
extent=(x_min - dx / 2, x_max + dx / 2, y_min - dy / 2,
y_max + dy / 2),
vmin=lowlim_2,
vmax=highlim_2,
)
ax21.set_title("Population on qubit 1")
ax22.set_title("Population on qubit 2")
ax21.set_xlabel("Coupler duration [ns]")
ax22.set_xlabel("Coupler duration [ns]")
ax21.set_ylabel("Coupler frequency [MHz]")
# ax22.set_ylabel("Coupler frequency [MHz]")
# ax22.yaxis.set_label_position("right")
# ax22.yaxis.tick_right()
for tick in ax22.get_yticklabels():
tick.set_visible(False)
# cb1 = fig2.colorbar(im1, ax=ax21)
# cb1.set_ticks([0, 1])
# cb2 = fig2.colorbar(im2, ax=ax22)
# cb2.set_ticks([0, 1])
# cb = fig2.colorbar(im1, ax=[ax21, ax22], use_gridspec=False)
fig2.show()
# Line cut at the center frequency
idx = np.argmax(np.mean(plot_data_1, axis=-1))
fig3, ax3 = plt.subplots(tight_layout=True)
ax3.plot(1e9 * coupler_ac_duration_arr,
plot_data_1[idx, :],
label="qubit 1")
ax3.plot(1e9 * coupler_ac_duration_arr,
plot_data_2[idx, :],
label="qubit 2")
ax3.set_xlabel("Coupler duration [ns]")
ax3.set_ylabel("Population")
fig3.show()
return fig2
def load(load_filename):
with h5py.File(load_filename, "r") as h5f:
num_averages = h5f.attrs["num_averages"]
readout_freq = h5f.attrs["readout_freq"]
control_freq = h5f.attrs["control_freq"]
readout_duration = h5f.attrs["readout_duration"]
control_duration = h5f.attrs["control_duration"]
match_duration = h5f.attrs["match_duration"]
readout_amp = h5f.attrs["readout_amp"]
control_amp = h5f.attrs["control_amp"]
sample_duration = h5f.attrs["sample_duration"]
wait_delay = h5f.attrs["wait_delay"]
readout_sample_delay = h5f.attrs["readout_sample_delay"]
readout_match_delay = h5f.attrs["readout_match_delay"]
t_arr = h5f["t_arr"][()]
store_arr = h5f["store_arr"][()]
match_i_data = h5f["match_i_data"][()]
match_q_data = h5f["match_q_data"][()]
source_code = h5f["source_code"][()]
# t_low = 1500 * 1e-9
# t_high = 2000 * 1e-9
# t_span = t_high - t_low
# idx_low = np.argmin(np.abs(t_arr - t_low))
# idx_high = np.argmin(np.abs(t_arr - t_high))
# idx = np.arange(idx_low, idx_high)
# nr_samples = len(idx)
nr_samples = len(t_arr)
t_span = nr_samples * (t_arr[1] - t_arr[0])
# Plot raw store data for first iteration as a check
fig1, ax1 = plt.subplots(2, 1, sharex=True, tight_layout=True)
ax11, ax12 = ax1
# ax11.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
# ax12.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax11.plot(1e9 * t_arr, np.abs(store_arr[0, 0, :]))
ax11.plot(1e9 * t_arr, np.abs(store_arr[1, 0, :]))
ax12.plot(1e9 * t_arr, np.angle(store_arr[0, 0, :]))
ax12.plot(1e9 * t_arr, np.angle(store_arr[1, 0, :]))
ax12.set_xlabel("Time [ns]")
fig1.show()
# # Analyze
data = match_i_data + 1j * match_q_data
data = rotate_opt(data)
data_g = data[0::2]
data_e = data[1::2]
x_g = data_g.real
y_g = data_g.imag
x_e = data_e.real
y_e = data_e.imag
std = max([x.std() for x in [x_g, y_g, x_e, y_e]])
x_min = min(x_g.mean(), x_e.mean()) - 5 * std
x_max = max(x_g.mean(), x_e.mean()) + 5 * std
y_min = min(y_g.mean(), y_e.mean()) - 5 * std
y_max = max(y_g.mean(), y_e.mean()) + 5 * std
H_g, xedges, yedges = np.histogram2d(x_g, y_g, bins=100, range=[[x_min, x_max], [y_min, y_max]], density=True)
H_e, xedges, yedges = np.histogram2d(x_e, y_e, bins=100, range=[[x_min, x_max], [y_min, y_max]], density=True)
H_g = H_g.T
H_e = H_e.T
z_max = max(H_g.max(), H_e.max())
# fig2, ax2 = plt.subplots(tight_layout=True)
# ax2.plot(match_i_data[0::2], match_q_data[0::2], '.')
# ax2.plot(match_i_data[1::2], match_q_data[1::2], '.')
# ax2.plot(np.mean(match_i_data[0::2]), np.mean(match_q_data[0::2]), '.')
# ax2.plot(np.mean(match_i_data[1::2]), np.mean(match_q_data[1::2]), '.')
# ax2.axhline(0.0, c="tab:gray", alpha=0.25)
# ax2.axvline(0.0, c="tab:gray", alpha=0.25)
# fig2.show()
fig3, ax3 = plt.subplots(1, 2, sharex=True, sharey=True, tight_layout=True, figsize=(9.6, 4.8))
ax31, ax32 = ax3
ax31.imshow(H_g, origin='lower', extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]], cmap="RdBu_r", vmin=-z_max, vmax=z_max)
ax32.imshow(H_e, origin='lower', extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]], cmap="RdBu_r", vmin=-z_max, vmax=z_max)
ax31.axhline(0.0, c="tab:gray", alpha=0.25)
ax31.axvline(0.0, c="tab:gray", alpha=0.25)
ax32.axhline(0.0, c="tab:gray", alpha=0.25)
ax32.axvline(0.0, c="tab:gray", alpha=0.25)
ax31.set_aspect('equal')
ax32.set_aspect('equal')
fig3.show()
xdata = 0.5 * (xedges[1:] + xedges[:-1])
fig4, ax4 = plt.subplots(tight_layout=True)
ax4.plot(xdata, np.sum(H_g, axis=0))
ax4.plot(xdata, np.sum(H_e, axis=0))
fig4.show()
return fig1, fig2
def measure_t1(which_qubit):
if which_qubit == 1:
readout_freq = readout_freq_1
control_freq = control_freq_1
control_amp = control_amp_pi_1
control_port = control_port_1
else:
readout_freq = readout_freq_2
control_freq = control_freq_2
control_amp = control_amp_pi_2
control_port = control_port_2
nr_delays = 128 # number of steps when changing delay between control and readout pulses
dt_delays = 4 * 1e-6 # s, step size when changing delay between control and readout pulses
with pulsed.Pulsed(
address=ADDRESS,
ext_ref_clk=EXT_REF_CLK,
adc_mode=AdcMode.Mixed,
adc_fsample=AdcFSample.G2,
dac_mode=[
DacMode.Mixed42, DacMode.Mixed02, DacMode.Mixed02,
DacMode.Mixed02
],
dac_fsample=[
DacFSample.G10, DacFSample.G6, DacFSample.G6, DacFSample.G6
],
) as pls:
pls.hardware.set_adc_attenuation(sample_port, 0.0)
pls.hardware.set_dac_current(readout_port, 32_000)
pls.hardware.set_dac_current(control_port, 32_000)
pls.hardware.set_inv_sinc(readout_port, 0)
pls.hardware.set_inv_sinc(control_port, 0)
pls.hardware.configure_mixer(
freq=readout_freq,
in_ports=sample_port,
out_ports=readout_port,
sync=False, # sync in next call
)
pls.hardware.configure_mixer(
freq=control_freq,
out_ports=control_port,
sync=True, # sync here
)
# ************************************
# *** Setup measurement parameters ***
# ************************************
# Setup lookup tables for frequencies
# we only need to use carrier 1
pls.setup_freq_lut(
output_ports=readout_port,
group=0,
frequencies=0.0,
phases=0.0,
phases_q=0.0,
)
pls.setup_freq_lut(
output_ports=control_port,
group=0,
frequencies=0.0,
phases=0.0,
phases_q=0.0,
)
# Setup lookup tables for amplitudes
pls.setup_scale_lut(
output_ports=readout_port,
group=0,
scales=readout_amp,
)
pls.setup_scale_lut(
output_ports=control_port,
group=0,
scales=control_amp,
)
# Setup readout and control pulses
# use setup_long_drive to create a pulse with square envelope
# setup_long_drive supports smooth rise and fall transitions for the pulse,
# but we keep it simple here
readout_pulse = pls.setup_long_drive(
output_port=readout_port,
group=0,
duration=readout_duration,
amplitude=1.0,
amplitude_q=1.0,
rise_time=0e-9,
fall_time=0e-9,
)
control_ns = int(round(
control_duration *
pls.get_fs("dac"))) # number of samples in the control template
control_envelope = sin2(control_ns)
control_pulse = pls.setup_template(
output_port=control_port,
group=0,
template=control_envelope,
template_q=control_envelope,
envelope=True,
)
# Setup sampling window
pls.set_store_ports(sample_port)
pls.set_store_duration(sample_duration)
# ******************************
# *** Program pulse sequence ***
# ******************************
T = 0.0 # s, start at time zero ...
for ii in range(nr_delays):
# pi pulse
pls.reset_phase(T, control_port)
pls.output_pulse(T, control_pulse)
# Readout pulse starts after control pulse,
# with an increasing delay
T += control_duration + ii * dt_delays
pls.reset_phase(T, readout_port)
pls.output_pulse(T, readout_pulse)
# Sampling window
pls.store(T + readout_sample_delay)
# Move to next iteration
T += readout_duration
T += wait_delay
# **************************
# *** Run the experiment ***
# **************************
pls.run(
period=T,
repeat_count=1,
num_averages=num_averages,
print_time=True,
)
t_arr, (data_I, data_Q) = pls.get_store_data()
store_arr = data_I + 1j * data_Q
idx_low = np.argmin(np.abs(t_arr - t_low))
idx_high = np.argmin(np.abs(t_arr - t_high))
idx = np.arange(idx_low, idx_high)
# Analyze T1
resp_arr = np.mean(store_arr[:, 0, idx], axis=-1)
resp_arr = rotate_opt(resp_arr)
delay_arr = dt_delays * np.arange(nr_delays)
# Fit data
popt_x, perr_x = t1_fit_simple(delay_arr, np.real(resp_arr))
T1 = popt_x[0]
T1_err = perr_x[0]
return T1, T1_err
def load(load_filename):
with h5py.File(load_filename, "r") as h5f:
num_averages = h5f.attrs["num_averages"]
control_freq_1 = h5f.attrs["control_freq_1"]
control_freq_2 = h5f.attrs["control_freq_2"]
control_if = h5f.attrs["control_if"]
readout_freq_1 = h5f.attrs["readout_freq_1"]
readout_freq_2 = h5f.attrs["readout_freq_2"]
readout_duration = h5f.attrs["readout_duration"]
control_duration = h5f.attrs["control_duration"]
readout_amp = h5f.attrs["readout_amp"]
control_amp_1 = h5f.attrs["control_amp_1"]
control_amp_2 = h5f.attrs["control_amp_2"]
sample_duration = h5f.attrs["sample_duration"]
wait_delay = h5f.attrs["wait_delay"]
readout_sample_delay = h5f.attrs["readout_sample_delay"]
coupler_dc_bias = h5f.attrs["coupler_dc_bias"]
nr_freqs = h5f.attrs["nr_freqs"]
nr_amps = h5f.attrs["nr_amps"]
coupler_ac_duration = h5f.attrs["coupler_ac_duration"]
t_arr = h5f["t_arr"][()]
store_arr = h5f["store_arr"][()]
coupler_ac_freq_arr = h5f["coupler_ac_freq_arr"][()]
coupler_ac_amp_arr = h5f["coupler_ac_amp_arr"][()]
t_low = 1500 * 1e-9
t_high = 2000 * 1e-9
idx_low = np.argmin(np.abs(t_arr - t_low))
idx_high = np.argmin(np.abs(t_arr - t_high))
idx = np.arange(idx_low, idx_high)
# # Plot raw store data for first iteration as a check
# fig0, ax0 = plt.subplots(2, 1, sharex=True, tight_layout=True)
# ax01, ax02 = ax0
# ax01.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
# ax02.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
# ax01.plot(1e9 * t_arr, np.abs(store_arr[0, 0, :]))
# ax02.plot(1e9 * t_arr, np.angle(store_arr[0, 0, :]))
# ax02.set_xlabel("Time [ns]")
# fig0.show()
resp_arr = np.mean(store_arr[:, 0, idx], axis=-1)
data = rotate_opt(resp_arr)
data.shape = (nr_amps, nr_freqs)
plot_data = data.real
# choose limits for colorbar
cutoff = 0.0 # %
lowlim = np.percentile(plot_data, cutoff)
highlim = np.percentile(plot_data, 100. - cutoff)
# extent
x_min = 1e-6 * coupler_ac_freq_arr[0]
x_max = 1e-6 * coupler_ac_freq_arr[-1]
dx = 1e-6 * (coupler_ac_freq_arr[1] - coupler_ac_freq_arr[0])
y_min = coupler_ac_amp_arr[0]
y_max = coupler_ac_amp_arr[-1]
dy = coupler_ac_amp_arr[1] - coupler_ac_amp_arr[0]
fig1, ax1 = plt.subplots(tight_layout=True)
im = ax1.imshow(
plot_data,
origin='lower',
aspect='auto',
interpolation='none',
extent=(x_min - dx / 2, x_max + dx / 2, y_min - dy / 2,
y_max + dy / 2),
vmin=lowlim,
vmax=highlim,
)
ax1.set_xlabel("Coupler frequency [MHz]")
ax1.set_ylabel("Coupler amplitude [FS]")
cb = fig1.colorbar(im)
cb.set_label("Response amplitude [FS]")
fig1.show()
return fig1
def analyze(self, all_plots: bool = False):
if self.t_arr is None:
raise RuntimeError
if self.store_arr is None:
raise RuntimeError
import matplotlib.pyplot as plt
ret_fig = []
idx = np.arange(IDX_LOW, IDX_HIGH)
t_low = self.t_arr[IDX_LOW]
t_high = self.t_arr[IDX_HIGH]
if all_plots:
# Plot raw store data for first iteration as a check
fig1, ax1 = plt.subplots(2, 1, sharex=True, tight_layout=True)
ax11, ax12 = ax1
ax11.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax12.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax11.plot(1e9 * self.t_arr, np.abs(self.store_arr[0, 0, :]))
ax12.plot(1e9 * self.t_arr, np.angle(self.store_arr[0, 0, :]))
ax12.set_xlabel("Time [ns]")
fig1.show()
ret_fig.append(fig1)
# Analyze T2
resp_arr = np.mean(self.store_arr[:, 0, idx], axis=-1)
data = rotate_opt(resp_arr)
# Fit data to I quadrature
try:
popt, perr = _fit_simple(self.delay_arr, np.real(data))
T2 = popt[2]
T2_err = perr[2]
print("T2 time: {} +- {} us".format(1e6 * T2, 1e6 * T2_err))
det = popt[3]
det_err = perr[3]
print("detuning: {} +- {} Hz".format(det, det_err))
success = True
except Exception as err:
print("Unable to fit data!")
print(err)
success = False
if all_plots:
fig2, ax2 = plt.subplots(4, 1, sharex=True, figsize=(6.4, 6.4), tight_layout=True)
ax21, ax22, ax23, ax24 = ax2
ax21.plot(1e6 * self.delay_arr, np.abs(data))
ax22.plot(1e6 * self.delay_arr, np.unwrap(np.angle(data)))
ax23.plot(1e6 * self.delay_arr, np.real(data))
if success:
ax23.plot(1e6 * self.delay_arr, _func(self.delay_arr, *popt), '--')
ax24.plot(1e6 * self.delay_arr, np.imag(data))
ax21.set_ylabel("Amplitude [FS]")
ax22.set_ylabel("Phase [rad]")
ax23.set_ylabel("I [FS]")
ax24.set_ylabel("Q [FS]")
ax2[-1].set_xlabel("Ramsey delay [us]")
fig2.show()
ret_fig.append(fig2)
data_max = np.abs(data.real).max()
unit = ""
mult = 1.0
if data_max < 1e-6:
unit = "n"
mult = 1e9
elif data_max < 1e-3:
unit = "μ"
mult = 1e6
elif data_max < 1e0:
unit = "m"
mult = 1e3
fig3, ax3 = plt.subplots(tight_layout=True)
ax3.plot(1e6 * self.delay_arr, mult * np.real(data), '.')
ax3.set_ylabel(f"I quadrature [{unit:s}FS]")
ax3.set_xlabel("Ramsey delay [μs]")
if success:
ax3.plot(1e6 * self.delay_arr, mult * _func(self.delay_arr, *popt), '--')
ax3.set_title(f"T2* = {1e6*T2:.0f} ± {1e6*T2_err:.0f} μs")
fig3.show()
ret_fig.append(fig3)
return ret_fig
def analyze(self, all_plots: bool = False):
assert self.t_arr is not None
assert self.store_arr is not None
assert self.control_freq_arr is not None
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
ret_fig = []
idx = np.arange(IDX_LOW, IDX_HIGH)
t_low = self.t_arr[IDX_LOW]
t_high = self.t_arr[IDX_HIGH]
if all_plots:
# Plot raw store data for first iteration as a check
fig1, ax1 = plt.subplots(2, 1, sharex=True, tight_layout=True)
ax11, ax12 = ax1
ax11.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax12.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax11.plot(1e9 * self.t_arr, np.abs(self.store_arr[0, 0, :]))
ax12.plot(1e9 * self.t_arr, np.angle(self.store_arr[0, 0, :]))
ax12.set_xlabel("Time [ns]")
fig1.show()
ret_fig.append(fig1)
# Analyze
resp_arr = np.mean(self.store_arr[:, 0, idx], axis=-1)
data = rotate_opt(resp_arr)
fig2, ax2 = plt.subplots(4,
1,
sharex=True,
figsize=(6.4, 6.4),
tight_layout=True)
ax21, ax22, ax23, ax24 = ax2
ax21.plot(1e-9 * self.control_freq_arr, np.abs(data))
ax22.plot(1e-9 * self.control_freq_arr, np.angle(data))
ax23.plot(1e-9 * self.control_freq_arr, np.real(data))
try:
data_min = data.real.min()
data_max = data.real.max()
data_rng = data_max - data_min
p0 = [
self.control_freq_center, self.control_freq_span / 4, data_rng,
data_min
]
popt, pcov = curve_fit(_gaussian, self.control_freq_arr, data.real,
p0)
ax23.plot(1e-9 * self.control_freq_arr,
_gaussian(self.control_freq_arr, *popt), '--')
print(f"f0 = {popt[0]} Hz")
print(f"sigma = {abs(popt[1])} Hz")
except Exception:
print("fit failed")
ax24.plot(1e-9 * self.control_freq_arr, np.imag(data))
ax21.set_ylabel("Amplitude [FS]")
ax22.set_ylabel("Phase [rad]")
ax23.set_ylabel("I [FS]")
ax24.set_ylabel("Q [FS]")
ax2[-1].set_xlabel("Control frequency [GHz]")
fig2.show()
ret_fig.append(fig2)
return ret_fig
def analyze(self, quantity: str = "quadrature", linecut: bool = False, blit: bool = True):
if self.control_freq_arr is None:
raise RuntimeError
if self.resp_arr is None:
raise RuntimeError
if quantity not in ["quadrature", "amplitude", "phase", "dB"]:
raise ValueError
import matplotlib.pyplot as plt
nr_amps = len(self.control_amp_arr)
self._AMP_IDX = nr_amps // 2
if quantity == "dB":
data = 20. * np.log10(np.abs(self.resp_arr))
unit = "dBFS"
title = "Response amplitude"
elif quantity == "phase":
data = np.angle(self.resp_arr)
unit = "rad"
title = "Response phase"
else:
if quantity == "amplitude":
data = np.abs(self.resp_arr)
title = "Response amplitude"
else: # "quadrature"
data = rotate_opt(self.resp_arr).real
title = "Response quadrature"
data_max = np.abs(data).max()
unit = ""
if data_max < 1e-6:
unit = "n"
data *= 1e9
elif data_max < 1e-3:
unit = "μ"
data *= 1e6
elif data_max < 1e0:
unit = "m"
data *= 1e3
unit += "FS"
amp_dBFS = 20 * np.log10(self.control_amp_arr / 1.0)
# choose limits for colorbar
cutoff = 1. # %
lowlim = np.percentile(data, cutoff)
highlim = np.percentile(data, 100. - cutoff)
# extent
x_min = 1e-9 * self.control_freq_arr[0]
x_max = 1e-9 * self.control_freq_arr[-1]
dx = 1e-9 * (self.control_freq_arr[1] - self.control_freq_arr[0])
y_min = amp_dBFS[0]
y_max = amp_dBFS[-1]
dy = amp_dBFS[1] - amp_dBFS[0]
if linecut:
fig1 = plt.figure(tight_layout=True, figsize=(6.4, 7.2))
gs = fig1.add_gridspec(3, 1)
ax1 = fig1.add_subplot(gs[:-1, 0])
else:
fig1 = plt.figure(tight_layout=True, figsize=(6.4, 4.8))
gs = fig1.add_gridspec(1, 1)
ax1 = fig1.add_subplot(gs[0, 0])
im = ax1.imshow(
data,
origin='lower',
aspect='auto',
interpolation='none',
extent=(x_min - dx / 2, x_max + dx / 2, y_min - dy / 2, y_max + dy / 2),
vmin=lowlim,
vmax=highlim,
)
if linecut:
line_sel = ax1.axhline(amp_dBFS[self._AMP_IDX], ls="--", c="k", lw=3, animated=blit)
ax1.set_title(f"Probe frequency: {self.readout_freq/1e9:.2f} GHz")
ax1.set_xlabel("Pump frequency [GHz]")
ax1.set_ylabel("Pump amplitude [dBFS]")
cb = fig1.colorbar(im)
cb.set_label(f"{title:s} [{unit:s}]")
if linecut:
ax2 = fig1.add_subplot(gs[-1, 0])
line_a, = ax2.plot(1e-9 * self.control_freq_arr, data[self._AMP_IDX], animated=blit)
f_min = 1e-9 * self.control_freq_arr.min()
f_max = 1e-9 * self.control_freq_arr.max()
f_rng = f_max - f_min
a_min = data.min()
a_max = data.max()
a_rng = a_max - a_min
ax2.set_xlim(f_min - 0.05 * f_rng, f_max + 0.05 * f_rng)
ax2.set_ylim(a_min - 0.05 * a_rng, a_max + 0.05 * a_rng)
ax2.set_xlabel("Frequency [GHz]")
ax2.set_ylabel(f"{title:s} [{unit:s}]")
def onbuttonpress(event):
if event.inaxes == ax1:
self._AMP_IDX = np.argmin(np.abs(amp_dBFS - event.ydata))
update()
def onkeypress(event):
if event.inaxes == ax1:
if event.key == "up":
self._AMP_IDX += 1
if self._AMP_IDX >= len(amp_dBFS):
self._AMP_IDX = len(amp_dBFS) - 1
update()
elif event.key == "down":
self._AMP_IDX -= 1
if self._AMP_IDX < 0:
self._AMP_IDX = 0
update()
def update():
line_sel.set_ydata([amp_dBFS[self._AMP_IDX], amp_dBFS[self._AMP_IDX]])
# ax1.set_title(f"amp = {amp_arr[self._AMP_IDX]:.2e}")
print(
f"drive amp {self._AMP_IDX:d}: {self.control_amp_arr[self._AMP_IDX]:.2e} FS = {amp_dBFS[self._AMP_IDX]:.1f} dBFS"
)
line_a.set_ydata(data[self._AMP_IDX])
# ax2.set_title("")
if blit:
fig1.canvas.restore_region(self._bg)
ax1.draw_artist(line_sel)
ax2.draw_artist(line_a)
fig1.canvas.blit(fig1.bbox)
# fig1.canvas.flush_events()
else:
fig1.canvas.draw()
fig1.canvas.mpl_connect('button_press_event', onbuttonpress)
fig1.canvas.mpl_connect('key_press_event', onkeypress)
fig1.show()
if linecut and blit:
fig1.canvas.draw()
# fig1.canvas.flush_events()
self._bg = fig1.canvas.copy_from_bbox(fig1.bbox)
ax1.draw_artist(line_sel)
ax2.draw_artist(line_a)
fig1.canvas.blit(fig1.bbox)
return fig1
def analyze(self, all_plots: bool = False):
assert self.t_arr is not None
assert self.store_arr is not None
assert self.control_freq_arr is not None
assert len(self.control_freq_arr) == self.control_freq_nr
import matplotlib.pyplot as plt
ret_fig = []
idx = np.arange(IDX_LOW, IDX_HIGH)
t_low = self.t_arr[IDX_LOW]
t_high = self.t_arr[IDX_HIGH]
if all_plots:
# Plot raw store data for first iteration as a check
fig1, ax1 = plt.subplots(2, 1, sharex=True, tight_layout=True)
ax11, ax12 = ax1
ax11.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax12.axvspan(1e9 * t_low, 1e9 * t_high, facecolor="#dfdfdf")
ax11.plot(1e9 * self.t_arr, np.abs(self.store_arr[0, 0, :]))
ax12.plot(1e9 * self.t_arr, np.angle(self.store_arr[0, 0, :]))
ax12.set_xlabel("Time [ns]")
fig1.show()
ret_fig.append(fig1)
# Analyze T1
resp_arr = np.mean(self.store_arr[:, 0, idx], axis=-1)
resp_arr.shape = (self.control_freq_nr, len(self.delay_arr))
data = rotate_opt(resp_arr)
plot_data = data.real
data_max = np.abs(plot_data).max()
unit = ""
mult = 1.0
if data_max < 1e-6:
unit = "n"
mult = 1e9
elif data_max < 1e-3:
unit = "μ"
mult = 1e6
elif data_max < 1e0:
unit = "m"
mult = 1e3
plot_data *= mult
# choose limits for colorbar
cutoff = 0.0 # %
lowlim = np.percentile(plot_data, cutoff)
highlim = np.percentile(plot_data, 100. - cutoff)
# extent
x_min = 1e+6 * self.delay_arr[0]
x_max = 1e+6 * self.delay_arr[-1]
dx = 1e+6 * (self.delay_arr[1] - self.delay_arr[0])
y_min = 1e-9 * self.control_freq_arr[0]
y_max = 1e-9 * self.control_freq_arr[-1]
dy = 1e-9 * (self.control_freq_arr[1] - self.control_freq_arr[0])
fig2, ax2 = plt.subplots(tight_layout=True)
im = ax2.imshow(
plot_data,
origin='lower',
aspect='auto',
interpolation='none',
extent=(x_min - dx / 2, x_max + dx / 2, y_min - dy / 2,
y_max + dy / 2),
vmin=lowlim,
vmax=highlim,
)
ax2.set_xlabel("Ramsey delay [μs]")
ax2.set_ylabel("Control frequency [GHz]")
cb = fig2.colorbar(im)
cb.set_label(f"Response I quadrature [{unit:s}FS]")
fig2.show()
ret_fig.append(fig2)
fit_freq = np.zeros_like(self.control_freq_arr)
for jj in range(self.control_freq_nr):
try:
res, _err = _fit_simple(self.delay_arr, plot_data[jj])
fit_freq[jj] = np.abs(res[3])
except Exception:
fit_freq[jj] = np.nan
n_fit = self.control_freq_nr // 4
pfit1 = np.polyfit(self.control_freq_arr[:n_fit], fit_freq[:n_fit], 1)
pfit2 = np.polyfit(self.control_freq_arr[-n_fit:], fit_freq[-n_fit:],
1)
x0 = np.roots(pfit1 - pfit2)[0]
fig3, ax3 = plt.subplots(tight_layout=True)
ax3.plot(self.control_freq_arr, fit_freq, '.')
ax3.set_ylabel("Fitted detuning [Hz]")
ax3.set_xlabel("Control frequency [Hz]$]")
fig3.show()
_lims = ax3.axis()
ax3.plot(
self.control_freq_arr,
np.polyval(pfit1, self.control_freq_arr),
'--',
c='tab:orange',
)
ax3.plot(
self.control_freq_arr,
np.polyval(pfit2, self.control_freq_arr),
'--',
c='tab:green',
)
ax3.axhline(0.0, ls='--', c='tab:gray')
ax3.axvline(x0, ls='--', c='tab:gray')
ax3.axis(_lims)
fig3.canvas.draw()
print(f"Fitted qubit frequency: {x0} Hz")
ret_fig.append(fig3)
return ret_fig
