matplotlib.colors.CenteredNorm#

class matplotlib.colors.CenteredNorm(vcenter=0, halfrange=None, clip=False)[source]#

Bases: 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.

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.

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.  ])

autoscale(A)[source]#

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

autoscale_None(A)[source]#

Set vmin and vmax.

property halfrange#

property vcenter#

property vmax#

Upper limit of the input data interval; maps to 1.

property vmin#

Lower limit of the input data interval; maps to 0.

Examples using matplotlib.colors.CenteredNorm#

[]

Colormap normalization

Colormap normalization