class matplotlib.colors.TwoSlopeNorm(vcenter, vmin=None, vmax=None)[source]#

Bases: Normalize

Normalize data with a set center.

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


The data value that defines 0.5 in the normalization.

vminfloat, optional

The data value that defines 0.0 in the normalization. Defaults to the min value of the dataset.

vmaxfloat, optional

The data value that defines 1.0 in the normalization. Defaults to the max value of the dataset.


This maps data value -4000 to 0., 0 to 0.5, and +10000 to 1.0; data between is linearly interpolated:

>>> import matplotlib.colors as mcolors
>>> offset = mcolors.TwoSlopeNorm(vmin=-4000.,
                                  vcenter=0., vmax=10000)
>>> data = [-4000., -2000., 0., 2500., 5000., 7500., 10000.]
>>> offset(data)
array([0., 0.25, 0.5, 0.625, 0.75, 0.875, 1.0])
__call__(value, clip=None)[source]#

Map value to the interval [0, 1]. The clip argument is unused.


Get vmin and vmax, and then clip at vcenter

property vcenter#

Examples using matplotlib.colors.TwoSlopeNorm#

Colormap Normalization

Colormap Normalization