matplotlib.colors.CenteredNorm

class matplotlib.colors.CenteredNorm(vcenter=0, halfrange=None, clip=False)

Bases: matplotlib.colors.Normalize

Normalize symmetrical data around a center (0 by default).

Unlike TwoSlopeNorm, CenteredNorm applies an equal rate of change around the center.

Useful when mapping symmetrical data around a conceptual center e.g., data that range from -2 to 4, with 0 as the midpoint, and with equal rates of change around that midpoint.

Parameters

vcenterfloat, default: 0

The data value that defines 0.5 in the normalization.

halfrangefloat, optional

The range of data values that defines a range of 0.5 in the normalization, so that vcenter - halfrange is 0.0 and vcenter + halfrange is 1.0 in the normalization. Defaults to the largest absolute difference to vcenter for the values in the dataset.

Examples

This maps data values -2 to 0.25, 0 to 0.5, and 4 to 1.0 (assuming equal rates of change above and below 0.0):

>>> import matplotlib.colors as mcolors
>>> norm = mcolors.CenteredNorm(halfrange=4.0)
>>> data = [-2., 0., 4.]
>>> norm(data)
array([0.25, 0.5 , 1.  ])

__call__(value, clip=None)

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).

autoscale(A)

Set halfrange to max(abs(A-vcenter)), then set vmin and vmax.

autoscale_None(A)

Set vmin and vmax.

property halfrange

property vcenter

Examples using matplotlib.colors.CenteredNorm

Colormap Normalization