def plot_dataset(dataset: qcodes.DataSet, parameter_names: Optional[list] = None, fig: Optional[int] = 1) -> None:
""" Plot a dataset to matplotlib figure window
Args:
dataset: DataSet to be plotted
parameter_names: List of arrays to be plotted
fig: Specification if Matplotlib figure window
"""
if parameter_names is None:
parameter_names = [dataset.default_parameter_name()]
if parameter_names == 'all':
parameter_names = [name for name in dataset.arrays.keys() if not dataset.arrays[name].is_setpoint]
default_array = dataset.default_parameter_array()
if fig:
plt.figure(fig)
plt.clf()
if len(default_array.shape) >= 2:
if len(parameter_names) > 1:
arrays = [dataset.default_parameter_array(parameter_name) for parameter_name in parameter_names]
plot_handle = MatPlot(*arrays, num=fig)
else:
plot_handle = MatPlot(dataset.default_parameter_array(parameter_names[0]), num=fig)
else:
for idx, parameter_name in enumerate(parameter_names):
if idx == 0:
plot_handle = MatPlot(dataset.default_parameter_array(parameter_name), num=fig)
else:
plot_handle.add(dataset.default_parameter_array(parameter_name,))
def _create_plot(i, name, data, counter_two):
# Step the color on all subplots no just on plots
# within the same axis/subplot
# this is to match the qcodes-pyqtplot behaviour.
title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"],
data.location_provider.counter)
rasterized_note = " rasterized plot full data available in datafile"
color = 'C' + str(counter_two)
counter_two += 1
plot = MatPlot()
inst_meas_name = "{}_{}".format(i._instrument.name, name)
inst_meas_data = getattr(data, inst_meas_name)
inst_meta_data = __get_plot_type(inst_meas_data, plot)
if 'z' in inst_meta_data:
xlen, ylen = inst_meta_data['z'].shape
rasterized = xlen * ylen > 5000
plot.add(inst_meas_data, rasterized=rasterized)
else:
rasterized = False
plot.add(inst_meas_data, color=color)
plot.subplots[0].grid()
if rasterized:
plot.subplots[0].set_title(title + rasterized_note)
else:
plot.subplots[0].set_title(title)
plot.save("{}_{:03d}.pdf".format(plot.get_default_title(),
counter_two))
plot.fig.canvas.draw()
def diffDataset(alldata, diff_dir='y', sigma=2, fig=None, meas_arr_name='measured'):
""" Differentiate a dataset and plot the result.
Args:
alldata (qcodes DataSet)
diff_dir (str): direction to differentiate in
meas_arr_name (str): name of the measured array to be differentiated
fig (int): the number for the figure to plot
sigma (float): parameter for gaussian filter kernel
"""
meas_arr_name = alldata.default_parameter_name(meas_arr_name)
meas_array = alldata.arrays[meas_arr_name]
imx = qtt.utilities.tools.diffImageSmooth(meas_array.ndarray, dy=diff_dir, sigma=sigma)
name = 'diff_dir_%s' % diff_dir
name = uniqueArrayName(alldata, name)
data_arr = qcodes.DataArray(
name=name, label=name, array_id=name, set_arrays=meas_array.set_arrays, preset_data=imx)
alldata.add_array(data_arr)
if fig is not None:
plt.figure(fig)
plt.clf()
plot = MatPlot(interval=0, num=fig)
plot.add(alldata.arrays[name])
plot.fig.axes[0].autoscale(tight=True)
plot.fig.axes[1].autoscale(tight=True)
return alldata
def _create_plot(i, name, data, counter_two, display_plot=True):
# Step the color on all subplots no just on plots
# within the same axis/subplot
# this is to match the qcodes-pyqtplot behaviour.
title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"],
data.location_provider.counter)
rasterized_note = " rasterized plot full data available in datafile"
color = 'C' + str(counter_two)
counter_two += 1
plot = MatPlot()
if issubclass(
i.__class__,
MultiChannelInstrumentParameter) or i._instrument is None:
inst_meas_name = name
else:
inst_meas_name = "{}_{}".format(i._instrument.name, name)
inst_meas_data = getattr(data, inst_meas_name)
try:
inst_meas_data = getattr(data, inst_meas_name)
except AttributeError:
inst_meas_name = "{}{}_0_0".format(i._instrument.name, name)
inst_meas_data = getattr(data, inst_meas_name)
inst_meta_data = __get_plot_type(inst_meas_data, plot)
if 'z' in inst_meta_data:
xlen, ylen = inst_meta_data['z'].shape
rasterized = xlen * ylen > 5000
po = plot.add(inst_meas_data, rasterized=rasterized)
auto_color_scale_from_config(po.colorbar, auto_color_scale,
inst_meta_data['z'],
cutoff_percentile)
else:
rasterized = False
plot.add(inst_meas_data, color=color)
plot.subplots[0].grid()
if rasterized:
plot.subplots[0].set_title(title + rasterized_note)
else:
plot.subplots[0].set_title(title)
title_list = plot.get_default_title().split(sep)
title_list.insert(-1, CURRENT_EXPERIMENT['pdf_subfolder'])
title = sep.join(title_list)
plot.rescale_axis()
plot.tight_layout()
plot.save("{}_{:03d}.pdf".format(title, counter_two))
if display_plot:
plot.fig.canvas.draw()
plt.show()
else:
plt.close(plot.fig)
def plot_flips(self , figsize=(8, 3)):
up_proportion_arrays = [
val for key, val in self.results.ESR.items()
if key.startswith('up_proportion') and 'idxs' not in key
]
assert len(up_proportion_arrays) >= 2
plot = MatPlot(up_proportion_arrays, marker='o', ms=5, linestyle='', figsize=figsize)
ax = plot[0]
ax.set_xlim(-0.5, len(up_proportion_arrays[0])-0.5)
ax.set_ylim(-0.015, 1.015)
ax.set_xlabel('Shot index')
ax.set_ylabel('Up proportion')
# Add threshold lines
ax.hlines(self.results.NMR.threshold_up_proportion, *ax.get_xlim(), lw=3)
ax.hlines(self.results.NMR.threshold_low, *ax.get_xlim(), color='grey', linestyle='--', lw=2)
ax.hlines(self.results.NMR.threshold_high, *ax.get_xlim(), color='grey', linestyle='--', lw=2)
if 'initialization' in self.results:
plot.add(self.results.initialization.up_proportions, marker='o', ms=8, linestyle='', color='grey', zorder=0, alpha=0.5)
initialization_filtered_shots = next(
val for key, val in self.results["initialization"].items()
if key.startswith("filtered_shots")
)
else:
initialization_filtered_shots = [True] * len(self.results.NMR.filtered_shots)
NMR_filtered_shots = self.results.NMR.filtered_shots
for k, up_proportion_tuple in enumerate(zip(*up_proportion_arrays)):
if not initialization_filtered_shots[k]:
color = 'orange'
elif not NMR_filtered_shots[k]:
color = 'red'
else:
color = 'green'
ax.plot([k, k], sorted(up_proportion_tuple)[-2:], color=color, zorder=-1)
plot.tight_layout()
# print contrast
sorted_up_proportions = np.sort(np.array(up_proportion_arrays), axis=0)
up_proportion = np.mean(sorted_up_proportions[-1])
dark_counts = np.mean(sorted_up_proportions[:-1])
contrast = up_proportion - dark_counts
print(f'Contrast = {up_proportion:.2f} - {dark_counts:.2f} = {contrast:.2f}')
return plot
def test_return_handle(self):
plotM = MatPlot(interval=0)
returned_handle = plotM.add([1, 2, 3])
line_handle = plotM[0].get_lines()[0]
self.assertIs(returned_handle, line_handle)
plotM.clear()
plt.close(plotM.fig)
time_range = time_range
fitting_num = X_num
x = np.linspace(0, fitting_num - 1, fitting_num)
t = np.linspace(0, time_range, fitting_num)
#pars1, pcov = curve_fit(sequence_decay, x, average[0],)
pars1, pcov = curve_fit(Exp_Sin_decay,
t * 1e6,
average[0],
p0=(0.2, 0.3, 6, 1.5, 0.3),
bounds=((0, -np.inf, 0.2, -np.inf, -np.inf),
(np.inf, np.inf, 10, np.inf, np.inf)))
pt = MatPlot()
pt.add(x=t, y=average[0], xlabel='dephasing_time', ylabel='probability |11>')
pt.add(x=t, y=sequence_decay(t * 1e6, pars1[0], pars1[1], pars1[-1]))
pt.add(
x=t,
y=Exp_Sin_decay(t * 1e6, pars1[0], pars1[1], pars1[2], pars1[3], pars1[4]),
)
print('T2* in exp1:', pars1[-1], 'us')
print('freq:', pars1[2], 'MHz')
#pars2, pcov = curve_fit(Exp_Sin_decay, x, average[1],)
pars2, pcov = curve_fit(Exp_Sin_decay,
t * 1e6,
average[1],
p0=(0.2, 0.3, 6, 1.5, 0.3),
bounds=((0, -np.inf, 0.2, -np.inf, -np.inf),
(np.inf, np.inf, 10, np.inf, np.inf)))
fitting_point = 51
x = np.linspace(0, 1.5e-6, fitting_point)[:fitting_point]
y = ds.probability_data[:, i, 0:fitting_point].mean(axis=0)
pars, pcov = curve_fit(T2_star_fitting,
x,
y,
p0=(0.8, 0.3, 4e6, 0, 0.5e-6),
bounds=((0.25, -np.inf, 1e6, -np.inf, 0),
(2, np.inf, 10e6, np.inf, 5)))
#pars, pcov = curve_fit(T2_star_fitting2, x, y,
# p0 = (0.8, 0.3, 0.5e-6),
# bounds = ((0.25,-np.inf,0),(2,np.inf,8e-6)))
#%%
pt = MatPlot()
#pt.add(x = x, y = T1_fitting(x,pars[0],pars[1],pars[2]))
pt.add(x=x, y=T2_star_fitting(x, pars[0], pars[1], pars[2], pars[3], pars[4]))
#pt.add(x = x, y = T2_star_fitting2(x,pars[0],pars[1],pars[2]))
pt.add(x=x,
y=ds.probability_data[:, i, start_point:start_point +
fitting_point].mean(axis=0))
print('T1 is: ', pars[0] * 1000, 'ms')
#pt1 = MatPlot()
#pt1.add_to_plot(x = x, y = T1_fitting(x,pars[0],pars[1],pars[2]),fmt='*')
#%%
x,
y,
p0=(0.9, 0.2, 0.3),
bounds=((0.5, 0, 0), (1, 0.8, 0.8)))
# bounds = ((), ()))
#pars, pcov = curve_fit(RB_Fidelity, x, y,
# p0 = (-0.9, 0.2, -0.4),
# bounds = ((-1, 0, -1),(-0.5, 0.8, 0)))
#%%
plot_point = fitting_point
pt = MatPlot()
pt.add(x=x[:plot_point],
y=ds.probability_data[:, i,
11:11 + fitting_point].mean(axis=0)[:plot_point],
fmt='bp',
xlabel='Clifford Numbers',
ylabel='$P_{|1>}$',
xunit='N',
yunit='%')
pt.add(
x=x[:plot_point],
y=RB_Fidelity(x, pars[0], pars[1], pars[2])[:plot_point],
fmt='b--',
)
#pt.add_to_plot(xlabel = 'Clifford Numbers')
#%%
#pt1 = MatPlot()
#pt1.add(z=ds.probability_data[:,i,11:11+fitting_point])
ds2 = DS2
x2 = np.array([len(clifford_sets[0][i]) for i in range(fitting_point)])
location_15crot = '2017-10-25/19-36-23/BillCoish_experimentAllXY_sequence'
location_3init = '2017-10-25/20-04-38/BillCoish_experimentAllXY_sequence'
#location = '2017-12-12/17-47-41/RB_experimentAllXY_sequence'
#%%
ds = load_data(location=location_3init, io=IO, formatter=formatter)
#%% average trace
x = ds.index3_set[0, 0, 0, 0, :]
i = 0
y = ds.raw_data[:, 0, i, :, :].mean(axis=(0, 1))
y = ds.raw_data[10, 0, i, :, :].mean(axis=0)
y = ds.raw_data[10, 0, i, 58, :]
#%%
'''
Qubit = 2
i = 0 if Qubit == 2 else 1
fitting_point = 59
x = np.linspace(1e-6, 25e-3, 60)[:fitting_point]
y = ds.probability_data[:,i,:fitting_point].mean(axis = 0)
pars, pcov = curve_fit(T1_fitting, x, y,)
'''
#%%
pt = MatPlot()
pt.add(x=x, y=y)
#print('T1 is: ', pars[0], 'ms')
pars1, pcov1 = curve_fit(RB_Fidelity, x[start:], P1[start:], p0 = (0.9, 0.2, 0), bounds = ((0.5, 0, 0),(1, 1, 0.001)))
pars2, pcov2 = curve_fit(RB_Fidelity, x[start:], P2[start:], p0 = (0.9, 0.2, 0), bounds = ((0.5, 0, 0),(1, 1, 0.001)))
pars3, pcov3 = curve_fit(RB_Fidelity, x[start:], P3[start:], p0 = (0.9, 0.2, 0), bounds = ((0.5, 0, 0),(1, 1, 0.001)))
P = 3/15 * (pars1[0] + pars2[0]) + 9/15 * pars3[0]
Fidelity = 1- (1-P)*3/4
print('Fidelity:', Fidelity)
#%%
import matplotlib as mpl
#plot_point = fitting_points
plot_points = fitting_points
pt = MatPlot()
pt.add(x = x[:plot_points],y = P1[:plot_points], fmt = 'rp',xlabel = 'Clifford Numbers', ylabel = '$P_{|1>}$', xunit = 'N', yunit = '%')
pt.add(x = x[:plot_points],y = P2[:plot_points], fmt = 'bp',xlabel = 'Clifford Numbers', ylabel = '$P_{|1>}$', xunit = 'N', yunit = '%')
pt.add(x = x[:plot_points],y = P3[:plot_points], fmt = 'gp',xlabel = 'Clifford Numbers', ylabel = '$P_{|1>}$', xunit = 'N', yunit = '%')
pt.add(x = x[:plot_points], y = RB_Fidelity(x,pars1[0],pars1[1],pars1[2])[:plot_points],fmt = 'r--', )
pt.add(x = x[:plot_points], y = RB_Fidelity(x,pars2[0],pars2[1],pars2[2])[:plot_points],fmt = 'b--', )
pt.add(x = x[:plot_points], y = RB_Fidelity(x,pars3[0],pars3[1],pars3[2])[:plot_points],fmt = 'g--', )
#%% character benchmarking with normalization
P_pi_q1 = 0.98
P_pi_q2 = 0.97
P_init_q1 = 1#0.99
P_init_q2 = 1#0.99
x,
y,
p0=(0.2, 1.5e6, 0, 0.3, 2e-6),
bounds=((0.3, 1e6, -np.pi, -1, 0),
(0.5, 2.5e6, np.pi, 1, 10e-6)))
y_fit = Exp_Sin(x, pars[0], pars[1], pars[2], pars[3], pars[4])
#%% fit resonance
pt1 = MatPlot()
pt1.add(x=x,
y=y,
fmt='bs',
xlabel='Frequecny shift',
ylabel='$P_{|1>}$',
xunit='MHz',
yunit='%')
pt1.add(
x=x1,
y=y_fit,
fmt='r--',
) # xlabel = 'Clifford Numbers', ylabel = 'probability |1>')#fmt='*')
#%% fit Rabi
pt1 = MatPlot()
#pt1.add_to_plot(x = x, y = y, fmt='bs')
#pt1.add_to_plot(x = x, y = y_fit, fmt = 'r--')#fmt='*')
