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195 changes: 94 additions & 101 deletions docs/tutorials/randomized_benchmarking.ipynb

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63 changes: 28 additions & 35 deletions docs/tutorials/t1.ipynb

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35 changes: 22 additions & 13 deletions docs/tutorials/t2hahn_characterization.ipynb

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44 changes: 23 additions & 21 deletions docs/tutorials/t2ramsey_characterization.ipynb

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2 changes: 1 addition & 1 deletion qiskit_experiments/curve_analysis/visualization/curves.py
Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,7 @@ def plot_curve_fit(
# Line
ax.plot(xs, unp.nominal_values(ys_fit_with_error), **plot_opts)

# Confidence interval of 1 sigma
# Confidence interval of N sigma values
stdev_arr = unp.std_devs(ys_fit_with_error)
if np.isfinite(stdev_arr).all():
for sigma, alpha in fit_uncertainty:
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,7 @@ def draw(
raw_sample=raw_samp,
fit_sample=fit_samp,
fit_data=fit_data,
style=style,
)

# add legend
Expand Down Expand Up @@ -221,6 +222,7 @@ def draw(
raw_sample=raw_samples[curve_ind],
fit_sample=fit_samples[curve_ind],
fit_data=fit_data,
style=style,
)

# add legend to each inset axis
Expand Down Expand Up @@ -292,6 +294,7 @@ def draw_single_curve_mpl(
raw_sample: CurveData,
fit_sample: CurveData,
fit_data: FitData,
style: PlotterStyle,
):
"""A function that draws a single curve on the given plotter canvas.

Expand All @@ -301,6 +304,7 @@ def draw_single_curve_mpl(
raw_sample: Raw sample data.
fit_sample: Formatted sample data.
fit_data: Fitting parameter collection.
style: Style sheet for plotting.
"""

# plot raw data if data is formatted
Expand Down Expand Up @@ -333,6 +337,7 @@ def draw_single_curve_mpl(
ax=axis,
color=series_def.plot_color,
zorder=2,
fit_uncertainty=style.plot_sigma,
)


Expand Down
2 changes: 1 addition & 1 deletion qiskit_experiments/curve_analysis/visualization/style.py
Original file line number Diff line number Diff line change
Expand Up @@ -41,5 +41,5 @@ class PlotterStyle:
# sigma values for confidence interval, which are the tuple of (sigma, alpha).
# the alpha indicates the transparency of the corresponding interval plot.
plot_sigma: List[Tuple[float, float]] = dataclasses.field(
default_factory=lambda: [(1.0, 0.3), (3.0, 0.1)]
default_factory=lambda: [(1.0, 0.7), (3.0, 0.3)]
)