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

Bases: matplotlib.colors.Normalize

Normalize a given value to the 0-1 range on a log scale.

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.

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

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).

__module__ = 'matplotlib.colors'
__slotnames__ = []
autoscale(self, A)[source]

Set vmin, vmax to min, max of A.

autoscale_None(self, A)[source]

If vmin or vmax are not set, use the min/max of A to set them.

inverse(self, value)[source]