Plot the integrated histogram for an array of data.

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

1.0 | | | ___| | | | _| | | | | ||_____________ 0.0

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.

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.

The axis that was plotted on.