class matplotlib.colors.AsinhNorm(linear_width=1, vmin=None, vmax=None, clip=False)[source]#

Bases: AsinhNorm

The inverse hyperbolic sine scale is approximately linear near the origin, but becomes logarithmic for larger positive or negative values. Unlike the SymLogNorm, the transition between these linear and logarithmic regions is smooth, which may reduce the risk of visual artifacts.


This API is provisional and may be revised in the future based on early user feedback.

linear_widthfloat, default: 1

The effective width of the linear region, beyond which the transformation becomes asymptotically logarithmic

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__(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).


Set vmin, vmax to min, max of A.


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


Examples using matplotlib.colors.AsinhNorm#

Colormap Normalizations SymLogNorm

Colormap Normalizations SymLogNorm

Colormap Normalizations SymLogNorm