Remove Sort1dExt in favor of SliceExt.

.github/workflows/ci.yml

@@ -20,7 +20,7 @@ jobs:
           - stable
           - beta
           - nightly
-          - 1.49.0  # MSRV
+          - 1.60.0  # MSRV
     steps:
       - uses: actions/checkout@v2
       - uses: actions-rs/toolchain@v1
@@ -32,6 +32,8 @@ jobs:
         run: cargo build --verbose
       - name: Run tests
         run: cargo test --verbose
+      - name: Run tests with Rayon
+        run: cargo test --verbose --features rayon
 
   cross_test:
     runs-on: ubuntu-latest
@@ -61,6 +63,8 @@ jobs:
         run: cross build --verbose --target=${{ matrix.target }}
       - name: Run tests
         run: cross test --verbose --target=${{ matrix.target }}
+      - name: Run tests with Rayon
+        run: cross test --verbose --features rayon --target=${{ matrix.target }}
 
   format:
     runs-on: ubuntu-latest

Cargo.toml

@@ -1,8 +1,9 @@
 [package]
 name = "ndarray-stats"
-version = "0.5.1"
+version = "0.6.0"
+rust-version = "1.60"
+edition = "2021"
 authors = ["Jim Turner <[email protected]>", "LukeMathWalker <[email protected]>"]
-edition = "2018"
 
 license = "MIT/Apache-2.0"
 
@@ -17,12 +18,13 @@ categories = ["data-structures", "science"]
 
 [dependencies]
 ndarray = "0.15.0"
+ndarray-slice = "0.2.2"
 noisy_float = "0.2.0"
 num-integer = "0.1"
 num-traits = "0.2"
 rand = "0.8.3"
 itertools = { version = "0.10.0", default-features = false }
-indexmap = "1.6.2"
+rayon = { version = "1.7.0", optional = true }
 
 [dev-dependencies]
 ndarray = { version = "0.15.0", features = ["approx"] }
@@ -33,6 +35,9 @@ approx = "0.4"
 quickcheck_macros = "1.0.0"
 num-bigint = "0.4.0"
 
+[features]
+rayon = ["dep:rayon", "ndarray-slice/rayon", "ndarray/rayon"]
+
 [[bench]]
 name = "sort"
 harness = false
@@ -44,3 +49,6 @@ harness = false
 [[bench]]
 name = "deviation"
 harness = false
+
+[profile.test]
+opt-level = 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 +42,8 @@ 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));
+                    let mut values = Vec::with_capacity(indices.len());
+                    black_box(arr.select_many_nth_unstable(&indices, &mut values));
                 },
                 BatchSize::SmallInput,
             )

src/histogram/bins.rs

@@ -3,6 +3,9 @@
 use ndarray::prelude::*;
 use std::ops::{Index, Range};
 
+#[cfg(feature = "rayon")]
+use rayon::slice::ParallelSliceMut;
+
 /// A sorted collection of type `A` elements used to represent the boundaries of intervals, i.e.
 /// [`Bins`] on a 1-dimensional axis.
 ///
@@ -30,11 +33,11 @@ use std::ops::{Index, Range};
 ///
 /// [`Bins`]: struct.Bins.html
 #[derive(Clone, Debug, Eq, PartialEq)]
-pub struct Edges<A: Ord> {
+pub struct Edges<A: Ord + Send> {
     edges: Vec<A>,
 }
 
-impl<A: Ord> From<Vec<A>> for Edges<A> {
+impl<A: Ord + Send> From<Vec<A>> for Edges<A> {
     /// Converts a `Vec<A>` into an `Edges<A>`, consuming the edges.
     /// The vector will be sorted in increasing order using an unstable sorting algorithm, with
     /// duplicates removed.
@@ -65,14 +68,17 @@ impl<A: Ord> From<Vec<A>> for Edges<A> {
     /// [pdqsort]: https://github.com/orlp/pdqsort
     fn from(mut edges: Vec<A>) -> Self {
         // sort the array in-place
+        #[cfg(feature = "rayon")]
+        edges.par_sort_unstable();
+        #[cfg(not(feature = "rayon"))]
         edges.sort_unstable();
         // remove duplicates
         edges.dedup();
         Edges { edges }
     }
 }
 
-impl<A: Ord + Clone> From<Array1<A>> for Edges<A> {
+impl<A: Ord + Send + Clone> From<Array1<A>> for Edges<A> {
     /// Converts an `Array1<A>` into an `Edges<A>`, consuming the 1-dimensional array.
     /// The array will be sorted in increasing order using an unstable sorting algorithm, with
     /// duplicates removed.
@@ -106,7 +112,7 @@ impl<A: Ord + Clone> From<Array1<A>> for Edges<A> {
     }
 }
 
-impl<A: Ord> Index<usize> for Edges<A> {
+impl<A: Ord + Send> Index<usize> for Edges<A> {
     type Output = A;
 
     /// Returns a reference to the `i`-th edge in `self`.
@@ -131,7 +137,7 @@ impl<A: Ord> Index<usize> for Edges<A> {
     }
 }
 
-impl<A: Ord> Edges<A> {
+impl<A: Ord + Send> Edges<A> {
     /// Returns the number of edges in `self`.
     ///
     /// # Examples
@@ -258,11 +264,11 @@ impl<A: Ord> Edges<A> {
 /// );
 /// ```
 #[derive(Clone, Debug, Eq, PartialEq)]
-pub struct Bins<A: Ord> {
+pub struct Bins<A: Ord + Send> {
     edges: Edges<A>,
 }
 
-impl<A: Ord> Bins<A> {
+impl<A: Ord + Send> Bins<A> {
     /// Returns a `Bins` instance where each bin corresponds to two consecutive members of the given
     /// [`Edges`], consuming the edges.
     ///

src/histogram/grid.rs

@@ -88,11 +88,11 @@ use std::ops::Range;
 /// [`GridBuilder`]: struct.GridBuilder.html
 /// [`strategy`]: strategies/index.html
 #[derive(Clone, Debug, Eq, PartialEq)]
-pub struct Grid<A: Ord> {
+pub struct Grid<A: Ord + Send> {
     projections: Vec<Bins<A>>,
 }
 
-impl<A: Ord> From<Vec<Bins<A>>> for Grid<A> {
+impl<A: Ord + Send> From<Vec<Bins<A>>> for Grid<A> {
     /// Converts a `Vec<Bins<A>>` into a `Grid<A>`, consuming the vector of bins.
     ///
     /// The `i`-th element in `Vec<Bins<A>>` represents the projection of the bin grid onto the
@@ -106,7 +106,7 @@ impl<A: Ord> From<Vec<Bins<A>>> for Grid<A> {
     }
 }
 
-impl<A: Ord> Grid<A> {
+impl<A: Ord + Send> Grid<A> {
     /// Returns the number of dimensions of the region partitioned by the grid.
     ///
     /// # Examples
@@ -220,7 +220,7 @@ impl<A: Ord> Grid<A> {
     }
 }
 
-impl<A: Ord + Clone> Grid<A> {
+impl<A: Ord + Send + Clone> Grid<A> {
     /// Given an `n`-dimensional index, `i = (i_0, ..., i_{n-1})`, returns an `n`-dimensional bin,
     /// `I_{i_0} x ... x I_{i_{n-1}}`, where `I_{i_j}` is the `i_j`-th interval on the `j`-th
     /// projection of the grid on the coordinate axes.
@@ -318,7 +318,7 @@ pub struct GridBuilder<B: BinsBuildingStrategy> {
 
 impl<A, B> GridBuilder<B>
 where
-    A: Ord,
+    A: Ord + Send,
     B: BinsBuildingStrategy<Elem = A>,
 {
     /// Returns a `GridBuilder` for building a [`Grid`] with a given [`strategy`] and some

src/histogram/histograms.rs

@@ -4,12 +4,12 @@ use ndarray::prelude::*;
 use ndarray::Data;
 
 /// Histogram data structure.
-pub struct Histogram<A: Ord> {
+pub struct Histogram<A: Ord + Send> {
     counts: ArrayD<usize>,
     grid: Grid<A>,
 }
 
-impl<A: Ord> Histogram<A> {
+impl<A: Ord + Send> Histogram<A> {
     /// Returns a new instance of Histogram given a [`Grid`].
     ///
     /// [`Grid`]: struct.Grid.html
@@ -43,7 +43,7 @@ impl<A: Ord> Histogram<A> {
     ///     [0, 1],
     /// ];
     /// assert_eq!(histogram_matrix, expected.into_dyn());
-    /// # Ok::<(), Box<std::error::Error>>(())
+    /// # Ok::<(), Box<dyn std::error::Error>>(())
     /// ```
     pub fn add_observation<S>(&mut self, observation: &ArrayBase<S, Ix1>) -> Result<(), BinNotFound>
     where
@@ -136,15 +136,15 @@ where
     /// ```
     fn histogram(&self, grid: Grid<A>) -> Histogram<A>
     where
-        A: Ord;
+        A: Ord + Send;
 
     private_decl! {}
 }
 
 impl<A, S> HistogramExt<A, S> for ArrayBase<S, Ix2>
 where
     S: Data<Elem = A>,
-    A: Ord,
+    A: Ord + Send,
 {
     fn histogram(&self, grid: Grid<A>) -> Histogram<A> {
         let mut histogram = Histogram::new(grid);

src/histogram/strategies.rs

@@ -65,7 +65,7 @@ use num_traits::{FromPrimitive, NumOps, Zero};
 /// [`Grid`]: ../struct.Grid.html
 pub trait BinsBuildingStrategy {
     #[allow(missing_docs)]
-    type Elem: Ord;
+    type Elem: Ord + Send;
     /// Returns a strategy that has learnt the required parameter fo building [`Bins`] for given
     /// 1-dimensional array, or an `Err` if it is not possible to infer the required parameter
     /// with the given data and specified strategy.
@@ -213,7 +213,7 @@ pub struct Auto<T> {
 
 impl<T> EquiSpaced<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     /// Returns `Err(BinsBuildError::Strategy)` if `bin_width<=0` or `min` >= `max`.
     /// Returns `Ok(Self)` otherwise.
@@ -256,7 +256,7 @@ where
 
 impl<T> BinsBuildingStrategy for Sqrt<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     type Elem = T;
 
@@ -292,7 +292,7 @@ where
 
 impl<T> Sqrt<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     /// The bin width (or bin length) according to the fitted strategy.
     pub fn bin_width(&self) -> T {
@@ -302,7 +302,7 @@ where
 
 impl<T> BinsBuildingStrategy for Rice<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     type Elem = T;
 
@@ -338,7 +338,7 @@ where
 
 impl<T> Rice<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     /// The bin width (or bin length) according to the fitted strategy.
     pub fn bin_width(&self) -> T {
@@ -348,7 +348,7 @@ where
 
 impl<T> BinsBuildingStrategy for Sturges<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     type Elem = T;
 
@@ -384,7 +384,7 @@ where
 
 impl<T> Sturges<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     /// The bin width (or bin length) according to the fitted strategy.
     pub fn bin_width(&self) -> T {
@@ -394,7 +394,7 @@ where
 
 impl<T> BinsBuildingStrategy for FreedmanDiaconis<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     type Elem = T;
 
@@ -433,7 +433,7 @@ where
 
 impl<T> FreedmanDiaconis<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     fn compute_bin_width(n_bins: usize, iqr: T) -> T {
         // casting `n_bins: usize` to `f64` may casus off-by-one error here if `n_bins` > 2 ^ 53,
@@ -451,7 +451,7 @@ where
 
 impl<T> BinsBuildingStrategy for Auto<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     type Elem = T;
 
@@ -504,7 +504,7 @@ where
 
 impl<T> Auto<T>
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     /// The bin width (or bin length) according to the fitted strategy.
     pub fn bin_width(&self) -> T {
@@ -524,7 +524,7 @@ where
 /// **Panics** if `n_bins == 0` and division by 0 panics for `T`.
 fn compute_bin_width<T>(min: T, max: T, n_bins: usize) -> T
 where
-    T: Ord + Clone + FromPrimitive + NumOps + Zero,
+    T: Ord + Send + Clone + FromPrimitive + NumOps + Zero,
 {
     let range = max - min;
     range / T::from_usize(n_bins).unwrap()

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;