class matplotlib.colors.FuncNorm(functions, vmin=None, vmax=None, clip=False)[source]

Bases: matplotlib.colors.FuncNorm

__call__(self, value, clip=None)

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


Data to normalize.


If None, defaults to self.clip (which defaults to False).


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

__init__(self, functions, vmin=None, vmax=None, clip=False)
vmin, vmaxfloat or None

If vmin and/or vmax is not given, they are initialized from the minimum and maximum value, respectively, of the first input processed; i.e., __call__(A) calls autoscale_None(A).

clipbool, default: False

If True values falling outside the range [vmin, vmax], are mapped to 0 or 1, whichever is closer, and masked values are set to 1. If False masked values remain masked.

Clipping silently defeats the purpose of setting the over, under, and masked colors in a colormap, so it is likely to lead to surprises; therefore the default is clip=False.


Returns 0 if vmin == vmax.

__module__ = 'matplotlib.colors'
__slotnames__ = []
inverse(self, value)

Examples using matplotlib.colors.FuncNorm