Make the entire crate compatible with the array reference type

Cargo.toml

@@ -14,13 +14,13 @@ repository = "https://github.com/rust-ndarray/ndarray-stats"
 documentation = "https://docs.rs/ndarray-stats/"
 readme = "README.md"
 
-description = "Statistical routines for ArrayBase, the n-dimensional array data structure provided by ndarray."
+description = "Statistical routines for the n-dimensional array data structures provided by ndarray."
 
 keywords = ["array", "multidimensional", "statistics", "matrix", "ndarray"]
 categories = ["data-structures", "science"]
 
 [dependencies]
-ndarray = "0.16.0"
+ndarray = "0.17.1"
 noisy_float = "0.2.0"
 num-integer = "0.1"
 num-traits = "0.2"
@@ -29,10 +29,10 @@ itertools = { version = "0.13", default-features = false }
 indexmap = "2.4"
 
 [dev-dependencies]
-ndarray = { version = "0.16.1", features = ["approx"] }
+ndarray = { version = "0.17.1", features = ["approx"] }
 criterion = "0.5.1"
 quickcheck = { version = "0.9.2", default-features = false }
-ndarray-rand = "0.15.0"
+ndarray-rand = "0.16.0"
 approx = "0.5"
 quickcheck_macros = "1.0.0"
 num-bigint = "0.4.0"

README.md

@@ -5,7 +5,7 @@
 [![Crate](https://img.shields.io/crates/v/ndarray-stats.svg)](https://crates.io/crates/ndarray-stats)
 [![Documentation](https://docs.rs/ndarray-stats/badge.svg)](https://docs.rs/ndarray-stats)
 
-This crate provides statistical methods for [`ndarray`]'s `ArrayBase` type.
+This crate provides statistical methods for [`ndarray`]'s `ArrayRef` type.
 
 Currently available routines include:
 - order statistics (minimum, maximum, median, quantiles, etc.);

benches/deviation.rs

@@ -12,8 +12,8 @@ fn sq_l2_dist(c: &mut Criterion) {
     group.plot_config(PlotConfiguration::default().summary_scale(AxisScale::Logarithmic));
     for len in &lens {
         group.bench_with_input(format!("{}", len), len, |b, &len| {
-            let data = Array::random(len, Uniform::new(0.0, 1.0));
-            let data2 = Array::random(len, Uniform::new(0.0, 1.0));
+            let data = Array::random(len, Uniform::new(0.0, 1.0).unwrap());
+            let data2 = Array::random(len, Uniform::new(0.0, 1.0).unwrap());
 
             b.iter(|| black_box(data.sq_l2_dist(&data2).unwrap()))
         });

benches/summary_statistics.rs

@@ -12,8 +12,8 @@ fn weighted_std(c: &mut Criterion) {
     group.plot_config(PlotConfiguration::default().summary_scale(AxisScale::Logarithmic));
     for len in &lens {
         group.bench_with_input(format!("{}", len), len, |b, &len| {
-            let data = Array::random(len, Uniform::new(0.0, 1.0));
-            let mut weights = Array::random(len, Uniform::new(0.0, 1.0));
+            let data = Array::random(len, Uniform::new(0.0, 1.0).unwrap());
+            let mut weights = Array::random(len, Uniform::new(0.0, 1.0).unwrap());
             weights /= weights.sum();
             b.iter_batched(
                 || data.clone(),

src/correlation.rs

@@ -1,14 +1,10 @@
 use crate::errors::EmptyInput;
 use ndarray::prelude::*;
-use ndarray::Data;
 use num_traits::{Float, FromPrimitive};
 
-/// Extension trait for `ArrayBase` providing functions
+/// Extension trait for `ndarray` providing functions
 /// to compute different correlation measures.
-pub trait CorrelationExt<A, S>
-where
-    S: Data<Elem = A>,
-{
+pub trait CorrelationExt<A> {
     /// Return the covariance matrix `C` for a 2-dimensional
     /// array of observations `M`.
     ///
@@ -125,10 +121,7 @@ where
     private_decl! {}
 }
 
-impl<A: 'static, S> CorrelationExt<A, S> for ArrayBase<S, Ix2>
-where
-    S: Data<Elem = A>,
-{
+impl<A: 'static> CorrelationExt<A> for ArrayRef2<A> {
     fn cov(&self, ddof: A) -> Result<Array2<A>, EmptyInput>
     where
         A: Float + FromPrimitive,
@@ -147,7 +140,7 @@ where
         let mean = self.mean_axis(observation_axis);
         match mean {
             Some(mean) => {
-                let denoised = self - &mean.insert_axis(observation_axis);
+                let denoised = self - mean.insert_axis(observation_axis);
                 let covariance = denoised.dot(&denoised.t());
                 Ok(covariance.mapv_into(|x| x / dof))
             }
@@ -208,7 +201,7 @@ mod cov_tests {
         let n_observations = 4;
         let a = Array::random(
             (n_random_variables, n_observations),
-            Uniform::new(-bound.abs(), bound.abs()),
+            Uniform::new(-bound.abs(), bound.abs()).unwrap(),
         );
         let covariance = a.cov(1.).unwrap();
         abs_diff_eq!(covariance, &covariance.t(), epsilon = 1e-8)
@@ -219,7 +212,10 @@ mod cov_tests {
     fn test_invalid_ddof() {
         let n_random_variables = 3;
         let n_observations = 4;
-        let a = Array::random((n_random_variables, n_observations), Uniform::new(0., 10.));
+        let a = Array::random(
+            (n_random_variables, n_observations),
+            Uniform::new(0., 10.).unwrap(),
+        );
         let invalid_ddof = (n_observations as f64) + rand::random::<f64>().abs();
         let _ = a.cov(invalid_ddof);
     }
@@ -299,7 +295,7 @@ mod pearson_correlation_tests {
         let n_observations = 4;
         let a = Array::random(
             (n_random_variables, n_observations),
-            Uniform::new(-bound.abs(), bound.abs()),
+            Uniform::new(-bound.abs(), bound.abs()).unwrap(),
         );
         let pearson_correlation = a.pearson_correlation().unwrap();
         abs_diff_eq!(