matplotlib.colors.TwoSlopeNorm#

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.

Parameters:

vcenterfloat

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.

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.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.

autoscale_None(A)[source]#

Get vmin and vmax.

If vcenter isn't in the range [vmin, vmax], either vmin or vmax is expanded so that vcenter lies in the middle of the modified range [vmin, vmax].

inverse(value)[source]#

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

Parameters:

value

Normalized value.

property vcenter#

Examples using matplotlib.colors.TwoSlopeNorm#

[]

Colormap normalization

Colormap normalization