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//! Univariate analysis

mod bootstrap;
mod percentiles;
mod resamples;
mod sample;

pub mod kde;
pub mod mixed;
pub mod outliers;

use floaty::Floaty;

use tuple::{Tuple, TupledDistributions};

use self::resamples::Resamples;

pub use self::percentiles::Percentiles;
pub use self::sample::Sample;

/// Performs a two-sample bootstrap
///
/// - Multithreaded
/// - Time: `O(nresamples)`
/// - Memory: `O(nresamples)`
#[cfg_attr(clippy, allow(cast_lossless))]
pub fn bootstrap<A, B, T, S>(
    a: &Sample<A>,
    b: &Sample<B>,
    nresamples: usize,
    statistic: S,
) -> T::Distributions where
    A: Floaty,
    B: Floaty,
    S: Fn(&Sample<A>, &Sample<B>) -> T,
    S: Sync,
    T: Tuple,
    T::Distributions: Send,
{
    //let ncpus = num_cpus::get();

    unsafe {
        // FIXME(#35) Parallel method always crashes on travis, and sometimes crashes on my laptop
        //// TODO need some sensible threshold to trigger the multi-threaded path
        //if ncpus > 1 && nresamples > a.as_slice().len() + b.as_slice().len() {
            //let granularity = nresamples / ncpus + 1;
            //let granularity_sqrt = (granularity as f64).sqrt().ceil() as usize;
            //let ref statistic = statistic;
            //let mut distributions: T::Distributions =
                //TupledDistributions::uninitialized(nresamples);

            //(0..ncpus).map(|i| {
                //// NB Can't implement `chunks_mut` for the tupled distributions without HKT,
                //// for now I'll make do with aliasing and careful non-overlapping indexing
                //let mut ptr = Unique::new(&mut distributions);
                //let offset = i * granularity;

                //thread::scoped(move || {
                    //let distributions: &mut T::Distributions = ptr.get_mut();
                    //let end = cmp::min(offset + granularity, nresamples);
                    //let mut a_resamples = Resamples::new(a);
                    //let mut b_resamples = Resamples::new(b);
                    //let mut i = offset;

                    //for _ in 0..granularity_sqrt {
                        //let a_resample = a_resamples.next();

                        //for _ in 0..granularity_sqrt {
                            //if i == end {
                                //return;
                            //}

                            //let b_resample = b_resamples.next();

                            //distributions.set_unchecked(i, statistic(a_resample, b_resample));

                            //i += 1;
                        //}
                    //}
                //})
            //}).collect::<Vec<_>>();

            //distributions
        //} else {
            let nresamples_sqrt = (nresamples as f64).sqrt().ceil() as usize;
            let mut a_resamples = Resamples::new(a);
            let mut b_resamples = Resamples::new(b);
            let mut distributions: T::Distributions =
                TupledDistributions::uninitialized(nresamples);

            let mut i = 0;
            'outer: for _ in 0..nresamples_sqrt {
                let a_resample = a_resamples.next();

                for _ in 0..nresamples_sqrt {
                    if i == nresamples {
                        break 'outer;
                    }

                    let b_resample = b_resamples.next();

                    distributions.set_unchecked(i, statistic(a_resample, b_resample));

                    i += 1;
                }
            }

            distributions
        //}
    }
}