SegmentedNorm

class SegmentedNorm(levels, vmin=None, vmax=None, clip=False)

Bases: Normalize

Normalizer that scales data linearly with respect to the interpolated index in an arbitrary monotonic level sequence.

Parameters:

• levels (sequence of float) – The level boundaries. Must be monotonically increasing or decreasing.

• vmin (float, optional) – Ignored but included for consistency with other normalizers. Set to the minimum of levels.

• vmax (float, optional) – Ignored but included for consistency with other normalizers. Set to the minimum of levels.

• clip (bool, optional) – Whether to clip values falling outside of the minimum and maximum of levels.

See also

ultraplot.constructor.Norm, ultraplot.colors.DiscreteNorm

Note

The algorithm this normalizer uses to select normalized values in-between level list indices is adapted from the algorithm LinearSegmentedColormap uses to select channel values in-between segment data points (hence the name SegmentedNorm).

Example

In the below example, unevenly spaced levels are passed to contourf, resulting in the automatic application of SegmentedNorm.

>>> import ultraplot as uplt
>>> import numpy as np
>>> levels = [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]
>>> data = 10 ** (3 * np.random.rand(10, 10))
>>> fig, ax = uplt.subplots()
>>> ax.contourf(data, levels=levels)

Methods Summary

__call__(value[, clip])	Normalize the data values to 0-1.
inverse(value)	Inverse of __call__.

Methods Documentation

__call__(value, clip=None)

Normalize the data values to 0-1. Inverse of inverse.

Parameters:

• value (numeric) – The data to be normalized.

• clip (bool, default: self.clip) – Whether to clip values falling outside of the minimum and maximum levels.

inverse(value)

Inverse of __call__.

Parameters:

value (numeric) – The data to be un-normalized.