matplotlib.colors.PowerNorm¶

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

Bases: matplotlib.colors.Normalize

Linearly map a given value to the 0-1 range and then apply a power-law normalization over that range.

Parameters:

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.

Notes

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.

Parameters:	value Data to normalize. clipbool If `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, gamma, vmin=None, vmax=None, clip=False)[source]¶

Parameters:

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.

Notes

Returns 0 if vmin == vmax.