criterion 0.1.1

use itertools;
use stats::univariate::Sample;
use stats::univariate::kde::kernel::Gaussian;
use stats::univariate::kde::{Bandwidth, Kde};

pub fn sweep(
    sample: &Sample<f64>,
    npoints: usize,
    range: Option<(f64, f64)>,
) -> (Box<[f64]>, Box<[f64]>) {
    let x_min = sample.min();
    let x_max = sample.max();

    let kde = Kde::new(sample, Gaussian, Bandwidth::Silverman);
    let h = kde.bandwidth();

    let (start, end) = match range {
        Some((start, end)) => (start, end),
        None => (x_min - 3. * h, x_max + 3. * h),
    };

    let xs: Vec<_> = itertools::linspace(start, end, npoints).collect();

    let ys = kde.map(&xs);

    (xs.into_boxed_slice(), ys)
}