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Plot the integrated histogram for an array of data.
cirq.integrated_histogram(
data: Union[Sequence[SupportsFloat], Mapping[Any, SupportsFloat]],
ax: Optional[plt.Axes] = None,
*,
cdf_on_x: bool = False,
axis_label: str = '',
semilog: bool = True,
median_line: bool = True,
median_label: Optional[str] = 'median',
mean_line: bool = False,
mean_label: Optional[str] = 'mean',
show_zero: bool = False,
title: Optional[str] = None,
**kwargs
) -> plt.Axes
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.
Returns | |
---|---|
The axis that was plotted on. |