Update ndarray in examples and docs

Author: kngwyu

README.md

@@ -99,7 +99,7 @@ crate-type = ["cdylib"]
 
 [dependencies]
 numpy = "0.7.0"
-ndarray = "0.12"
+ndarray = "0.13"
 
 [dependencies.pyo3]
 version = "0.8"

examples/linalg/Cargo.toml

@@ -10,7 +10,7 @@ crate-type = ["cdylib"]
 
 [dependencies]
 numpy = { path = "../.." }
-ndarray = "0.12"
+ndarray = ">= 0.12"
 ndarray-linalg = { version = "0.10", features = ["openblas"] }
 
 [dependencies.pyo3]

examples/simple-extension/Cargo.toml

@@ -10,7 +10,7 @@ crate-type = ["cdylib"]
 
 [dependencies]
 numpy = { path = "../.." }
-ndarray = "0.12"
+ndarray = ">= 0.12"
 
 [dependencies.pyo3]
 version = "0.8"

src/array.rs

@@ -50,7 +50,7 @@ use crate::types::{NpyDataType, TypeNum};
 /// All data types you can use implements [TypeNum](../types/trait.TypeNum.html).
 ///
 /// Dimensions are represented by ndarray's
-/// [Dimension](https://docs.rs/ndarray/0.12/ndarray/trait.Dimension.html) trait.
+/// [Dimension](https://docs.rs/ndarray/latest/ndarray/trait.Dimension.html) trait.
 ///
 /// Typically, you can use `Ix1, Ix2, ..` for fixed size arrays, and use `IxDyn` for dynamic
 /// dimensioned arrays. They're re-exported from `ndarray` crate.
@@ -59,7 +59,7 @@ use crate::types::{NpyDataType, TypeNum};
 /// or [`PyArrayDyn`](./type.PyArrayDyn.html).
 ///
 /// To specify concrete dimension like `3×4×5`, you can use types which implements ndarray's
-/// [`IntoDimension`](https://docs.rs/ndarray/0.12/ndarray/dimension/conversion/trait.IntoDimension.html)
+/// [`IntoDimension`](https://docs.rs/ndarray/latest/ndarray/dimension/conversion/trait.IntoDimension.html)
 /// trait. Typically, you can use array(e.g. `[3, 4, 5]`) or tuple(e.g. `(3, 4, 5)`) as a dimension.
 ///
 /// # Example
@@ -497,7 +497,7 @@ impl<T: TypeNum, D: Dimension> PyArray<T, D> {
     }
 
     /// Construct PyArray from
-    /// [`ndarray::Array`](https://docs.rs/ndarray/0.12/ndarray/type.Array.html).
+    /// [`ndarray::Array`](https://docs.rs/ndarray/latest/ndarray/type.Array.html).
     ///
     /// This method uses internal [`Vec`](https://doc.rust-lang.org/std/vec/struct.Vec.html)
     /// of `ndarray::Array` as numpy array.
@@ -516,7 +516,7 @@ impl<T: TypeNum, D: Dimension> PyArray<T, D> {
     }
 
     /// Get the immutable view of the internal data of `PyArray`, as
-    /// [`ndarray::ArrayView`](https://docs.rs/ndarray/0.12/ndarray/type.ArrayView.html).
+    /// [`ndarray::ArrayView`](https://docs.rs/ndarray/latest/ndarray/type.ArrayView.html).
     ///
     /// # Example
     /// ```
@@ -659,7 +659,7 @@ impl<T: TypeNum, D: Dimension> PyArray<T, D> {
 
 impl<T: TypeNum + Clone, D: Dimension> PyArray<T, D> {
     /// Get a copy of `PyArray` as
-    /// [`ndarray::Array`](https://docs.rs/ndarray/0.12/ndarray/type.Array.html).
+    /// [`ndarray::Array`](https://docs.rs/ndarray/latest/ndarray/type.Array.html).
     ///
     /// # Example
     /// ```

src/convert.rs

@@ -150,7 +150,7 @@ impl<D: Dimension> ToNpyDims for D {
 /// Types that can be used to index an array.
 ///
 /// See
-/// [IntoDimension](https://docs.rs/ndarray/0.12/ndarray/dimension/conversion/trait.IntoDimension.html)
+/// [IntoDimension](https://docs.rs/ndarray/latest/ndarray/dimension/conversion/trait.IntoDimension.html)
 /// for what types you can use as `NpyIndex`.
 ///
 /// But basically, you can use