Struct ScaleColorSteps 

pub struct ScaleColorSteps { /* private fields */ }

Binned (stepped) continuous colour scale — R’s scale_*_steps / scale_*_stepsn / scale_*_fermenter.

A continuous variable is bucketed into n_bins equal-width bins over the trained domain, and each bin is filled with a discrete colour sampled from the gradient. It reports as discrete, so the legend shows one stepped swatch per bin (a guide_bins/guide_coloursteps-style legend).

Implementations

impl ScaleColorSteps

pub fn new( aesthetic: Aesthetic, stops: Vec<(u8, u8, u8)>, n_bins: usize, ) -> Self

Build from evenly-spaced gradient stops (>= 1) and a bin count.

pub fn two( aesthetic: Aesthetic, low: (u8, u8, u8), high: (u8, u8, u8), n_bins: usize, ) -> Self

Two-colour low→high binned scale.

Trait Implementations

impl Clone for ScaleColorSteps

fn clone(&self) -> ScaleColorSteps

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Scale for ScaleColorSteps

fn aesthetic(&self) -> Aesthetic

Which aesthetic this scale is for.

fn train(&mut self, values: &[Value])

Incorporate data values to determine domain.

fn map(&self, _value: &Value) -> f64

Map a data value to a [0, 1] normalized position (position scales) or to a concrete visual value index (color/size scales).

fn breaks(&self) -> Vec<(f64, String)>

Generate break positions and labels for axis/legend.

fn name(&self) -> &str

Human-readable name (axis title).

fn set_name(&mut self, name: &str)

Set the scale name.

fn is_discrete(&self) -> bool

Whether this is a discrete scale.

fn map_to_color(&self, value: &Value) -> Option<(u8, u8, u8)>

Map a data value to an RGB color. Default returns None.

fn domain(&self) -> Option<(f64, f64)>

Return the trained data domain (min, max) for continuous scales. Used by the colorbar legend to pass data-space values to map_to_color().

fn clone_box(&self) -> Box<dyn Scale>

Clone this scale into a boxed trait object.

fn reset_training(&mut self)

Reset training state so the scale can be retrained on new data.

fn transform(&self, value: &Value) -> Value

Apply transformation to raw data (e.g., log10).

fn map_to_shape(&self, _value: &Value) -> Option<PointShape>

Map a data value to a point shape. Default returns None.

fn map_to_linetype(&self, _value: &Value) -> Option<Linetype>

Map a data value to a linetype. Default returns None.

fn map_to_size(&self, _value: &Value) -> Option<f64>

Map a data value to a point size (radius in pixels). Default returns None.

fn map_to_alpha(&self, _value: &Value) -> Option<f64>

Map a data value to an alpha (opacity) value. Default returns None.

fn sec_axis(&self) -> Option<&SecAxis>

Get the secondary axis specification, if any.

fn set_limits(&mut self, _min: f64, _max: f64)

Override the trained domain limits (used by coord_cartesian zoom).

fn filter_limits(&self) -> Option<(f64, f64)>

Return OOB filter limits if this scale was created with explicit limits (e.g., via xlim/ylim). Data outside these limits should be removed before stats.