ColorFunctionBinning
is an option for plotting functions that divides values into a limited set of bins for styling.
Details
- Possible settings for ColorFunctionBinning include:
-
None continuous range of values Automatic automatically determined bins n approximately n equally spaced divisions "KMeans" use the k-means clustering algorithm to find the bins "Quantile" use quantiles to compute the bins {d} use bins with width d {min,max,d} use bins from min to max with width d {{b1,b2,…}} use specific break points bi to separate the bins {"name",n} find n divisions using the named method f use f[{v1,v2,…}] to determine the bins - With binned color functions, the color function is sampled evenly based on the number of bins, not on their values.
Examples
open allclose allBasic Examples (2)
Scope (5)
Specify how large the bins should be:
Use the k-means algorithm to find clusters of values:
Specify how many clusters to find:
Using "Quantile" will put approximately the same number of regions in each bin:
Some of the regions are small and may not be visible:
Text
Wolfram Research (2020), ColorFunctionBinning, Wolfram Language function, https://reference.wolfram.com/language/ref/ColorFunctionBinning.html.
CMS
Wolfram Language. 2020. "ColorFunctionBinning." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/ColorFunctionBinning.html.
APA
Wolfram Language. (2020). ColorFunctionBinning. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ColorFunctionBinning.html