Document the Fortran memory layout of NumPy arrays created from nalgebra matrices and test the conversion of the naglebra vector aliases.

Author: adamreichold

src/convert.rs

@@ -194,6 +194,10 @@ where
     type Item = N;
     type Dim = crate::Ix2;
 
+    /// Note that the NumPy array always has Fortran memory layout
+    /// matching the [memory layout][memory-layout] used by [`nalgebra`].
+    ///
+    /// [memory-layout]: https://nalgebra.org/docs/faq/#what-is-the-memory-layout-of-matrices
     fn to_pyarray<'py>(&self, py: Python<'py>) -> &'py PyArray<Self::Item, Self::Dim> {
         unsafe {
             let array = PyArray::<N, _>::new(py, (self.nrows(), self.ncols()), true);

tests/to_py.rs

@@ -322,4 +322,20 @@ fn matrix_to_numpy() {
 
         assert_eq!(array.readonly().as_array(), array![[0, 1, 2]]);
     });
+
+    let vector = nalgebra::Vector4::<i32>::new(-4, 1, 2, 3);
+
+    Python::with_gil(|py| {
+        let array = vector.to_pyarray(py);
+
+        assert_eq!(array.readonly().as_array(), array![[-4], [1], [2], [3]]);
+    });
+
+    let vector = nalgebra::RowVector2::<i32>::new(23, 42);
+
+    Python::with_gil(|py| {
+        let array = vector.to_pyarray(py);
+
+        assert_eq!(array.readonly().as_array(), array![[23, 42]]);
+    });
 }