Suppose the input is a list of gate fidelities. The x-axis of the plot will
be gate fidelity, and the y-axis will be the probability that a random gate
fidelity from the list is less than the x-value. It will look something like
this
Another way of saying this is that we assume the probability distribution
function (pdf) of gate fidelities is a set of equally weighted delta
functions at each value in the list. Then, the "integrated histogram"
is the cumulative distribution function (cdf) for this pdf.
Args
data
Data to histogram. If the data is a Mapping, we histogram the
values. All nans will be removed.
ax
The axis to plot on. If None, we generate one.
cdf_on_x
If True, flip the axes compared the above example.
axis_label
Label for x axis (y-axis if cdf_on_x is True).
semilog
If True, force the x-axis to be logarithmic.
median_line
If True, draw a vertical line on the median value.
median_label
If drawing median line, optional label for it.
mean_line
If True, draw a vertical line on the mean value.
mean_label
If drawing mean line, optional label for it.
title
Title of the plot. If None, we assign "N={len(data)}".
show_zero
If True, moves the step plot up by one unit by prepending 0
to the data.
**kwargs
Kwargs to forward to ax.step(). Some examples are
color: Color of the line.
linestyle: Linestyle to use for the plot.
lw: linewidth for integrated histogram.
ms: marker size for a histogram trace.
label: An optional label which can be used in a legend.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2026-10-08 UTC."],[],[]]