Remove Sort1dExt in favor of SliceExt.

Reduces worst-case complexity from O(n^2) to O(n log(n)).

Author: n3vu0r

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "ndarray-stats"
-version = "0.5.1"
+version = "0.6.0"
 authors = ["Jim Turner <[email protected]>", "LukeMathWalker <[email protected]>"]
 edition = "2018"
 
@@ -17,6 +17,7 @@ categories = ["data-structures", "science"]
 
 [dependencies]
 ndarray = "0.15.0"
+ndarray-slice = "0.1.1"
 noisy_float = "0.2.0"
 num-integer = "0.1"
 num-traits = "0.2"

benches/sort.rs

@@ -2,7 +2,7 @@ use criterion::{
     black_box, criterion_group, criterion_main, AxisScale, BatchSize, Criterion, PlotConfiguration,
 };
 use ndarray::prelude::*;
-use ndarray_stats::Sort1dExt;
+use ndarray_slice::Slice1Ext;
 use rand::prelude::*;
 
 fn get_from_sorted_mut(c: &mut Criterion) {
@@ -19,7 +19,7 @@ fn get_from_sorted_mut(c: &mut Criterion) {
                 || Array1::from(data.clone()),
                 |mut arr| {
                     for &i in &indices {
-                        black_box(arr.get_from_sorted_mut(i));
+                        black_box(arr.select_nth_unstable(i));
                     }
                 },
                 BatchSize::SmallInput,
@@ -42,7 +42,7 @@ fn get_many_from_sorted_mut(c: &mut Criterion) {
             b.iter_batched(
                 || Array1::from(data.clone()),
                 |mut arr| {
-                    black_box(arr.get_many_from_sorted_mut(&indices));
+                    black_box(arr.select_many_nth_unstable(&indices));
                 },
                 BatchSize::SmallInput,
             )

src/lib.rs

@@ -35,7 +35,6 @@ pub use crate::entropy::EntropyExt;
 pub use crate::histogram::HistogramExt;
 pub use crate::maybe_nan::{MaybeNan, MaybeNanExt};
 pub use crate::quantile::{interpolate, Quantile1dExt, QuantileExt};
-pub use crate::sort::Sort1dExt;
 pub use crate::summary_statistics::SummaryStatisticsExt;
 
 #[cfg(test)]
@@ -105,5 +104,4 @@ pub mod errors;
 pub mod histogram;
 mod maybe_nan;
 mod quantile;
-mod sort;
 mod summary_statistics;

src/quantile/mod.rs

@@ -1,10 +1,11 @@
 use self::interpolate::{higher_index, lower_index, Interpolate};
-use super::sort::get_many_from_sorted_mut_unchecked;
 use crate::errors::QuantileError;
 use crate::errors::{EmptyInput, MinMaxError, MinMaxError::UndefinedOrder};
 use crate::{MaybeNan, MaybeNanExt};
+use indexmap::IndexMap;
 use ndarray::prelude::*;
 use ndarray::{Data, DataMut, RemoveAxis, Zip};
+use ndarray_slice::Slice1Ext;
 use noisy_float::types::N64;
 use std::cmp;
 
@@ -471,15 +472,20 @@ where
                     searched_indexes.push(higher_index(q, axis_len));
                 }
             }
-            searched_indexes.sort();
-            searched_indexes.dedup();
+            let mut indexes = Array1::from_vec(searched_indexes);
+            indexes.sort_unstable();
+            let (indexes, _duplicates) = indexes.partition_dedup();
 
             let mut results = Array::from_elem(results_shape, data.first().unwrap().clone());
             Zip::from(results.lanes_mut(axis))
                 .and(data.lanes_mut(axis))
                 .for_each(|mut results, mut data| {
-                    let index_map =
-                        get_many_from_sorted_mut_unchecked(&mut data, &searched_indexes);
+                    let (lower_values, _higher) = data.select_many_nth_unstable(&indexes);
+                    let index_map = indexes
+                        .iter()
+                        .copied()
+                        .zip(lower_values.iter().map(|(_lower, value)| (*value).clone()))
+                        .collect::<IndexMap<usize, A>>();
                     for (result, &q) in results.iter_mut().zip(qs) {
                         let lower = if I::needs_lower(q, axis_len) {
                             Some(index_map[&lower_index(q, axis_len)].clone())