Version 3.1.1
matplotlib
Fork me on GitHub

Table of Contents

matplotlib.colors.DivergingNorm

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

Bases: matplotlib.colors.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.

Parameters:
vcenter : float

The data value that defines 0.5 in the normalization.

vmin : float, optional

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

vmax : float, optional

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

Examples

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.DivergingNorm(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])
autoscale_None(self, A)[source]

Get vmin and vmax, and then clip at vcenter

Examples using matplotlib.colors.DivergingNorm