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

Values within the range [vmin, vmax] from the input data will be linearly mapped to [0, 1]. If either vmin or vmax is not provided, they default to the minimum and maximum values of the input, respectively.

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, resulting in values outside [0, 1]. This behavior is usually desirable, as colormaps can mark these under and over values with specific colors.

If clipping is on, values below vmin are mapped to 0 and values above vmax are mapped to 1. Such values become indistinguishable from regular boundary values, which may cause misinterpretation of the data.


If vmin == vmax, input data will be mapped to 0.

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

Normalize the data and return the normalized data.


Data to normalize.

clipbool, optional

See the description of the parameter clip in 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).


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


Maps the normalized value (i.e., index in the colormap) back to image data value.


Normalized value.

Examples using matplotlib.colors.AsinhNorm#

Colormap normalizations SymLogNorm

Colormap normalizations SymLogNorm