Bases: Normalize
Normalizer that scales data linearly with respect to the interpolated index in an arbitrary monotonic level sequence.
levels (sequence of float) – The level boundaries. Must be monotonically increasing or decreasing.
vcenter (float, default: None) – The central colormap value. Default is to omit this.
vmin (float, optional) – Ignored but included for consistency. Set to min(levels).
vmax (float, optional) – Ignored but included for consistency. Set to max(levels).
clip (bool, optional) – Whether to clip values falling outside of vmin and vmax.
fair (bool, optional) – Whether to use fair scaling. See DivergingNorm.
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 proplot as pplt >>> 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 = pplt.subplots() >>> ax.contourf(data, levels=levels)
Methods Summary
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4