|View source on GitHub|
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.
|The axis that was plotted on.|