# matplotlib.colors.BoundaryNorm¶

class matplotlib.colors.BoundaryNorm(boundaries, ncolors, clip=False, *, extend='neither')[source]

Generate a colormap index based on discrete intervals.

Unlike Normalize or LogNorm, BoundaryNorm maps values to integers instead of to the interval 0-1.

Mapping to the 0-1 interval could have been done via piece-wise linear interpolation, but using integers seems simpler, and reduces the number of conversions back and forth between integer and floating point.

Parameters: boundariesarray-likeMonotonically increasing sequence of at least 2 boundaries. ncolorsintNumber of colors in the colormap to be used. clipbool, optionalIf clip is True, out of range values are mapped to 0 if they are below boundaries[0] or mapped to ncolors - 1 if they are above boundaries[-1]. If clip is False, out of range values are mapped to -1 if they are below boundaries[0] or mapped to ncolors if they are above boundaries[-1]. These are then converted to valid indices by Colormap.__call__. extend{'neither', 'both', 'min', 'max'}, default: 'neither'Extend the number of bins to include one or both of the regions beyond the boundaries. For example, if extend is 'min', then the color to which the region between the first pair of boundaries is mapped will be distinct from the first color in the colormap, and by default a Colorbar will be drawn with the triangle extension on the left or lower end. int16 scalar or array

Notes

boundaries defines the edges of bins, and data falling within a bin is mapped to the color with the same index.

If the number of bins, including any extensions, is less than ncolors, the color index is chosen by linear interpolation, mapping the [0, nbins - 1] range onto the [0, ncolors - 1] range.

__call__(self, value, clip=None)[source]

Normalize value data in the [vmin, vmax] interval into the [0.0, 1.0] interval and return it.

Parameters: valueData to normalize. clipboolIf None, defaults to self.clip (which defaults to False).

Notes

If not already initialized, self.vmin and self.vmax are initialized using self.autoscale_None(value).

__init__(self, boundaries, ncolors, clip=False, *, extend='neither')[source]
Parameters: boundariesarray-likeMonotonically increasing sequence of at least 2 boundaries. ncolorsintNumber of colors in the colormap to be used. clipbool, optionalIf clip is True, out of range values are mapped to 0 if they are below boundaries[0] or mapped to ncolors - 1 if they are above boundaries[-1]. If clip is False, out of range values are mapped to -1 if they are below boundaries[0] or mapped to ncolors if they are above boundaries[-1]. These are then converted to valid indices by Colormap.__call__. extend{'neither', 'both', 'min', 'max'}, default: 'neither'Extend the number of bins to include one or both of the regions beyond the boundaries. For example, if extend is 'min', then the color to which the region between the first pair of boundaries is mapped will be distinct from the first color in the colormap, and by default a Colorbar will be drawn with the triangle extension on the left or lower end. int16 scalar or array

Notes

boundaries defines the edges of bins, and data falling within a bin is mapped to the color with the same index.

If the number of bins, including any extensions, is less than ncolors, the color index is chosen by linear interpolation, mapping the [0, nbins - 1] range onto the [0, ncolors - 1] range.

__module__ = 'matplotlib.colors'
__slotnames__ = []
inverse(self, value)[source]
Raises: ValueErrorBoundaryNorm is not invertible, so calling this method will always raise an error