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

Determines the behavior for mapping values outside the range [vmin, vmax].

If clipping is off, values outside the range [vmin, vmax] are also transformed by the power function, resulting in values outside [0, 1]. For a standard use with colormaps, this behavior is desired because colormaps mark these outside values with specific colors for over or under.

If True values falling outside the range [vmin, vmax], are mapped to 0 or 1, whichever is closer. This makes these values indistinguishable from regular boundary values and can lead to misinterpretation of the data.

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

Determines the behavior for mapping values outside the range [vmin, vmax].

If clipping is off, values outside the range [vmin, vmax] are also transformed linearly, resulting in values outside [0, 1]. For a standard use with colormaps, this behavior is desired because colormaps mark these outside values with specific colors for over or under.