PixelMap

eitprocessing.plotting.pixelmap.PixelMapPlotting `dataclass` ¶

PixelMapPlotting(pixel_map: PixelMap | PixelMask)

Utility class for plotting pixel maps and masks.

config `property` ¶

config: PixelMapPlotConfig

Plotting configuration for pixel maps and pixel masks.

imshow ¶

imshow(
    colorbar: bool | None = None,
    normalize: bool | None = None,
    percentage: bool | None = None,
    absolute: bool | None = None,
    colorbar_kwargs: dict | None = None,
    facecolor: ColorType | None = None,
    hide_axes: bool | None = None,
    **kwargs
) -> AxesImage

Display the pixel map or mask using imshow.

This method is a wrapper around matplotlib.pyplot.imshow that provides convenient defaults and formatting options for displaying pixel maps and masks.

Plotting configuration is taken from plot_config, unless overridden by explicit arguments. Any additional keyword arguments are merged with plot_config.extra_kwargs and passed to matplotlib.pyplot.imshow.

If colorbar is True, a colorbar is added to the axes. If normalize is True, the pixel values are scaled by their maximum value before plotting. The appearance of the colorbar can be modified using percentage and absolute flags:

percentage=True displays the colorbar in percentage units (where 1.0 → 100%).
absolute=True uses the absolute value of the data for color scaling and labeling.

If the colormap has underflow or overflow colors (e.g., for negative values), the colorbar will extend accordingly. Additional arguments can be passed to control or override the appearance of the colorbar via colorbar_kwargs, which are passed directly to matplotlib.pyplot.colorbar.

If hide_axes is True, the axis ticks and labels are hidden (but the axes remain visible to retain background styling such as facecolor).

Additional keyword arguments are passed directly to imshow, allowing for full control over image rendering. Notably, you can pass an existing matplotlib Axes object using the ax keyword argument.

PARAMETER	DESCRIPTION
`colorbar`	Whether to display a colorbar. TYPE: `bool \| None` DEFAULT: `None`
`normalize`	Whether to scale by the maximum value. TYPE: `bool \| None` DEFAULT: `None`
`percentage`	Whether to display the colorbar values as a percentage. TYPE: `bool \| None` DEFAULT: `None`
`absolute`	Whether to display the colorbar using absolute values. TYPE: `bool \| None` DEFAULT: `None`
`colorbar_kwargs`	Additional arguments passed to `matplotlib.pyplot.colorbar`. TYPE: `dict` DEFAULT: `None`
`facecolor`	Background color for the axes. If None, uses the facecolor of the PixelMap. TYPE: `ColorType \| None` DEFAULT: `None`
`hide_axes`	Whether to hide the axes ticks and labels. TYPE: `bool \| None` DEFAULT: `None`
`ax`	Axes to plot on. If not provided, uses the current axes. TYPE: `Axes`
`**kwargs`	Additional keyword arguments passed to `matplotlib.pyplot.imshow`. DEFAULT: `{}`

RETURNS	DESCRIPTION
`AxesImage`	The image object created by imshow. TYPE: `AxesImage`

contour ¶

contour(**kwargs) -> ContourSet

Create a contour plot of the pixel map or mask.

This method uses matplotlib.pyplot.contour to create a contour plot of the pixel map values.

PARAMETER	DESCRIPTION
`**kwargs`	Additional keyword arguments passed to `matplotlib.pyplot.contour`. DEFAULT: `{}`

RETURNS	DESCRIPTION
`QuadContourSet`	The contour set created by the contour function. TYPE: `ContourSet`

surface ¶

surface(**kwargs) -> Poly3DCollection

Create a 3D surface plot of the pixel map or mask.

This method uses matplotlib.pyplot.axes3d.plot_surface to create a 3D surface plot of the pixel map values.

PARAMETER	DESCRIPTION
`**kwargs`	Additional keyword arguments passed to `matplotlib.pyplot.axes3d.plot_surface`. DEFAULT: `{}`

RETURNS	DESCRIPTION
`Axes3D`	The 3D axes object containing the surface plot. TYPE: `Poly3DCollection`

add_region_markers ¶

add_region_markers(
    ax: Axes, label_map: dict | None = None, **kwargs
) -> list[Text]

Add markers to an integer map plot.

This methods adds text labels at the center of all pixels with the same label, assuming continuous regions where the center of mass is inside the region itself. If the regions are not continuous, the label is placed in the largest region. If the center of mass of the region is outside the region, the label position is determined by repeatedly eroding the region (using scipy.ndimage.erosion).

By default, the text labels are the integer values converted to strings. You can customize labels by providing a dictionary label_map, mapping the integer values to custom labels.

Center of mass

For any regular shape, the center of mass is near the geometrical center of a region. However, for e.g. a C-shaped region, the center of mass might fall outside the region itself.

PARAMETER	DESCRIPTION
`ax`	The axes to add the markers to. TYPE: `Axes`
`label_map`	A dictionary mapping integer labels to string labels. If None, the integer label itself is used. TYPE: `dict \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments passed to the text function, such as `color` and `fontsize`. DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Text]`	list[mpl.text.Text]: A list of text objects created for the labels.