# matplotlib.colors.PowerNorm#

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

Bases: Normalize

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

Parameters:
gammafloat

Power law exponent.

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 remain masked.

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

Notes

The normalization formula is

$\left ( \frac{x - v_{min}}{v_{max} - v_{min}} \right )^{\gamma}$
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 and under 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__(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, optional

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).

inverse(value)[source]#

## Examples using matplotlib.colors.PowerNorm#

Colormap normalizations

Colormap normalizations