cirq.Heatmap

Distribution of a value in 2D qubit lattice as a color map.

cirq.Heatmap(
    value_map: Union[Mapping[QubitTuple, SupportsFloat], Mapping[grid_qubit.GridQubit,
        SupportsFloat]],
    **kwargs
)

Used in the notebooks

Used in the tutorials
QVM Stabilizer Example Qubit Picking Heatmaps Identifying Hardware Changes

Args
`value_map`	A dictionary of qubits or QubitTuples as keys and corresponding magnitude as float values. It corresponds to the data which should be plotted as a heatmap.
`**kwargs`	Optional kwargs including title: str, default = None plot_colorbar: bool, default = True annotation_map: dictionary, A dictionary of QubitTuples as keys and corresponding annotation str as values. It corresponds to the text that should be added on top of each heatmap polygon unit. annotation_format: str, default = '.2g' Formatting string using which annotation_map will be implicitly constructed by applying format(value, annotation_format) for each key in value_map. This is ignored if annotation_map is explicitly specified. annotation_text_kwargs: Matplotlib Text kwargs, highlighted_qubits: An iterable of qubits to highlight. colorbar_position: {'right', 'left', 'top', 'bottom'}, default = 'right' colorbar_size: str, default = '5%' colorbar_pad: str, default = '2%' colorbar_options: Matplotlib colorbar kwargs, default = None, collection_options: Matplotlib PolyCollection **kwargs, default {"cmap" : "viridis"} vmin, vmax: colormap scaling floats, default = None

Args

value_map A dictionary of qubits or QubitTuples as keys and corresponding magnitude as float values. It corresponds to the data which should be plotted as a heatmap.

**kwargs

Optional kwargs including title: str, default = None plot_colorbar: bool, default = True

annotation_map: dictionary, A dictionary of QubitTuples as keys and corresponding annotation str as values. It corresponds to the text that should be added on top of each heatmap polygon unit. annotation_format: str, default = '.2g' Formatting string using which annotation_map will be implicitly constructed by applying format(value, annotation_format) for each key in value_map. This is ignored if annotation_map is explicitly specified. annotation_text_kwargs: Matplotlib Text **kwargs, highlighted_qubits: An iterable of qubits to highlight.

colorbar_position: {'right', 'left', 'top', 'bottom'}, default = 'right' colorbar_size: str, default = '5%' colorbar_pad: str, default = '2%' colorbar_options: Matplotlib colorbar **kwargs, default = None,

collection_options: Matplotlib PolyCollection **kwargs, default {"cmap" : "viridis"} vmin, vmax: colormap scaling floats, default = None

Methods

`plot`

View source

plot(
    ax: Optional[plt.Axes] = None, **kwargs
) -> Tuple[plt.Axes, mpl_collections.Collection]

Plots the heatmap on the given Axes.

Args
`ax`	the Axes to plot on. If not given, a new figure is created, plotted on, and shown.
`**kwargs`	The optional keyword arguments are used to temporarily override the values present in the heatmap config. See init for more details on the allowed arguments.

Returns
A 2-tuple `(ax, collection)`. `ax` is the `plt.Axes` that is plotted on. `collection` is the collection of paths drawn and filled.

`update_config`

View source

update_config(
    **kwargs
) -> 'Heatmap'

Add/Modify **kwargs args passed during initialisation.