/// Applies Largest-Triangle-Three-Buckets to a numeric time series for visualization-oriented downsampling. The
/// algorithm preserves the first and last points, partitions the remaining input points based on the requested
/// output count, and selects points that preserve the visual shape of the series. Results are selected input
/// timestamp/value points, not fixed-width bucket aggregate rows.
#[derive(
    Debug,
    Clone,
    conjure_object::serde::Serialize,
    conjure_object::serde::Deserialize,
    conjure_object::private::DeriveWith
)]
#[serde(crate = "conjure_object::serde")]
#[derive_with(PartialEq, Eq, PartialOrd, Ord, Hash)]
#[conjure_object::private::staged_builder::staged_builder]
#[builder(crate = conjure_object::private::staged_builder, update, inline)]
pub struct NumericLargestTriangleThreeBucketsSeriesNode {
    #[builder(custom(type = super::NumericSeriesNode, convert = Box::new))]
    #[serde(rename = "input")]
    input: Box<super::NumericSeriesNode>,
    #[serde(rename = "maxPointsToReturn")]
    max_points_to_return: i32,
}
impl NumericLargestTriangleThreeBucketsSeriesNode {
    /// Constructs a new instance of the type.
    #[inline]
    pub fn new(input: super::NumericSeriesNode, max_points_to_return: i32) -> Self {
        Self::builder().input(input).max_points_to_return(max_points_to_return).build()
    }
    #[inline]
    pub fn input(&self) -> &super::NumericSeriesNode {
        &*self.input
    }
    #[inline]
    pub fn max_points_to_return(&self) -> i32 {
        self.max_points_to_return
    }
}
