Realscale

Realscale is a term used in data processing and visualization to describe methods for scaling real-valued data to a common reference range or scale. It denotes monotone transformations that map the real numbers to a predefined interval, typically to enable comparison across features or to fit display coordinates, while preserving the order and relative differences between values.

Common implementations include linear realscale, which uses known bounds a and b and maps x to (x − a)/(b − a); robust realscale, which uses robust statistics such as the median and interquartile range to reduce the influence of outliers; and percentile-based scales, which set bounds at specific quantiles. Nonlinear variants, such as symmetric or logarithmic-like mappings, may also be described as realscale in practice.

Realscale is widely used in data visualization to place values on axes, in user interfaces to proportionally render gauges and sliders, and in preprocessing pipelines where features must be limited to a standard range. The choice of scaling method affects interpretability, sensitivity to outliers, and the ability to invert the transformation.

Properties and considerations: a realscale is typically monotone and invertible if bounds are fixed; it may be linear or nonlinear. When bounds are not known ahead of time, adaptive estimation is used, such as expanding ranges as new data arrive. The term is applied variably across fields, so specific definitions may differ by context.

See also: normalization, scaling, axis scale, data visualization.