Eigen support for special matrix objects

Functions returning specialized Eigen matrices like Eigen::DiagonalMatrix and
Eigen::SelfAdjointView--which inherit from EigenBase but not
DenseBase--isn't currently allowed; such classes are explicitly copyable
into a Matrix (by definition), and so we can support functions that
return them by copying the value into a Matrix then casting that
resulting dense Matrix into a numpy.ndarray.  This commit does exactly
that.

docs/advanced.rst

@@ -1098,6 +1098,14 @@ pybind11 will automatically and transparently convert
 1. Static and dynamic Eigen dense vectors and matrices to instances of
 1. Static and dynamic Eigen dense vectors and matrices to instances of
    ``numpy.ndarray`` (and vice versa).
    ``numpy.ndarray`` (and vice versa).
 
 
+1. Returned matrix expressions such as blocks (including columns or rows) and
+   diagonals will be converted to ``numpy.ndarray`` of the expression
+   values.
+
+1. Returned matrix-like objects such as Eigen::DiagonalMatrix or
+   Eigen::SelfAdjointView will be converted to ``numpy.ndarray`` containing the
+   expressed value.
+
 1. Eigen sparse vectors and matrices to instances of
 1. Eigen sparse vectors and matrices to instances of
    ``scipy.sparse.csr_matrix``/``scipy.sparse.csc_matrix`` (and vice versa).
    ``scipy.sparse.csr_matrix``/``scipy.sparse.csc_matrix`` (and vice versa).
 
 
@@ -1107,11 +1115,14 @@ them somehow, in which case the information won't be propagated to the caller.
 
 
 .. code-block:: cpp
 .. code-block:: cpp
 
 
-    /* The Python bindings of this function won't replicate
-       the intended effect of modifying the function argument */
+    /* The Python bindings of these functions won't replicate
+       the intended effect of modifying the function arguments */
     void scale_by_2(Eigen::Vector3f &v) {
     void scale_by_2(Eigen::Vector3f &v) {
         v *= 2;
         v *= 2;
     }
     }
+    void scale_by_2(Eigen::Ref<Eigen::MatrixXd> &v) {
+        v *= 2;
+    }
 
 
 To see why this is, refer to the section on :ref:`opaque` (although that
 To see why this is, refer to the section on :ref:`opaque` (although that
 section specifically covers STL data types, the underlying issue is the same).
 section specifically covers STL data types, the underlying issue is the same).

example/eigen.cpp

@@ -66,6 +66,22 @@ void init_eigen(py::module &m) {
         return x.block(start_row, start_col, block_rows, block_cols);
         return x.block(start_row, start_col, block_rows, block_cols);
     });
     });
 
 
+    // Returns a DiagonalMatrix with diagonal (1,2,3,...)
+    m.def("incr_diag", [](int k) {
+        Eigen::DiagonalMatrix<int, Eigen::Dynamic> m(k);
+        for (int i = 0; i < k; i++) m.diagonal()[i] = i+1;
+        return m;
+    });
+
+    // Returns a SelfAdjointView referencing the lower triangle of m
+    m.def("symmetric_lower", [](const Eigen::MatrixXi &m) {
+            return m.selfadjointView<Eigen::Lower>();
+    });
+    // Returns a SelfAdjointView referencing the lower triangle of m
+    m.def("symmetric_upper", [](const Eigen::MatrixXi &m) {
+            return m.selfadjointView<Eigen::Upper>();
+    });
+
     m.def("fixed_r", [mat]() -> FixedMatrixR { 
     m.def("fixed_r", [mat]() -> FixedMatrixR { 
         return FixedMatrixR(mat);
         return FixedMatrixR(mat);
     });
     });

example/eigen.py

@@ -14,6 +14,7 @@ from example import double_mat_cm, double_mat_rm
 from example import cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6
 from example import cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6
 from example import diagonal, diagonal_1, diagonal_n
 from example import diagonal, diagonal_1, diagonal_n
 from example import block
 from example import block
+from example import incr_diag, symmetric_upper, symmetric_lower
 try:
 try:
     import numpy as np
     import numpy as np
     import scipy
     import scipy
@@ -88,3 +89,20 @@ for i in range(-5, 7):
 print("block(2,1,3,3) %s" % ("OK" if (block(ref, 2, 1, 3, 3) == ref[2:5, 1:4]).all() else "FAILED"))
 print("block(2,1,3,3) %s" % ("OK" if (block(ref, 2, 1, 3, 3) == ref[2:5, 1:4]).all() else "FAILED"))
 print("block(1,4,4,2) %s" % ("OK" if (block(ref, 1, 4, 4, 2) == ref[1:, 4:]).all() else "FAILED"))
 print("block(1,4,4,2) %s" % ("OK" if (block(ref, 1, 4, 4, 2) == ref[1:, 4:]).all() else "FAILED"))
 print("block(1,4,3,2) %s" % ("OK" if (block(ref, 1, 4, 3, 2) == ref[1:4, 4:]).all() else "FAILED"))
 print("block(1,4,3,2) %s" % ("OK" if (block(ref, 1, 4, 3, 2) == ref[1:4, 4:]).all() else "FAILED"))
+
+print("incr_diag %s" % ("OK" if (incr_diag(7) == np.diag([1,2,3,4,5,6,7])).all() else "FAILED"))
+
+asymm = np.array([
+    [1,  2, 3, 4],
+    [5,  6, 7, 8],
+    [9, 10,11,12],
+    [13,14,15,16]])
+symm_lower = np.array(asymm)
+symm_upper = np.array(asymm)
+for i in range(4):
+    for j in range(i+1, 4):
+        symm_lower[i,j] = symm_lower[j,i]
+        symm_upper[j,i] = symm_upper[i,j]
+
+print("symmetric_lower %s" % ("OK" if (symmetric_lower(asymm) == symm_lower).all() else "FAILED"))
+print("symmetric_upper %s" % ("OK" if (symmetric_upper(asymm) == symm_upper).all() else "FAILED"))

example/eigen.ref

@@ -50,3 +50,6 @@ diagonal_n(6) OK
 block(2,1,3,3) OK
 block(2,1,3,3) OK
 block(1,4,4,2) OK
 block(1,4,4,2) OK
 block(1,4,3,2) OK
 block(1,4,3,2) OK
+incr_diag OK
+symmetric_lower OK
+symmetric_upper OK

include/pybind11/eigen.h

@@ -61,6 +61,19 @@ public:
     static constexpr bool value = decltype(test(std::declval<T>()))::value;
     static constexpr bool value = decltype(test(std::declval<T>()))::value;
 };
 };
 
 
+// Test for objects inheriting from EigenBase<Derived> that aren't captured by the above.  This
+// basically covers anything that can be assigned to a dense matrix but that don't have a typical
+// matrix data layout that can be copied from their .data().  For example, DiagonalMatrix and
+// SelfAdjointView fall into this category.
+template <typename T> class is_eigen_base {
+private:
+    template<typename Derived> static std::true_type test(const Eigen::EigenBase<Derived> &);
+    static std::false_type test(...);
+public:
+    static constexpr bool value = !is_eigen_dense<T>::value && !is_eigen_sparse<T>::value &&
+        decltype(test(std::declval<T>()))::value;
+};
+
 template<typename Type>
 template<typename Type>
 struct type_caster<Type, typename std::enable_if<is_eigen_dense<Type>::value && !is_eigen_ref<Type>::value>::type> {
 struct type_caster<Type, typename std::enable_if<is_eigen_dense<Type>::value && !is_eigen_ref<Type>::value>::type> {
     typedef typename Type::Scalar Scalar;
     typedef typename Type::Scalar Scalar;
@@ -164,11 +177,10 @@ protected:
 
 
 template<typename Type>
 template<typename Type>
 struct type_caster<Type, typename std::enable_if<is_eigen_dense<Type>::value && is_eigen_ref<Type>::value>::type> {
 struct type_caster<Type, typename std::enable_if<is_eigen_dense<Type>::value && is_eigen_ref<Type>::value>::type> {
-private:
+protected:
     using Derived = typename std::remove_const<typename is_eigen_ref<Type>::Derived>::type;
     using Derived = typename std::remove_const<typename is_eigen_ref<Type>::Derived>::type;
     using DerivedCaster = type_caster<Derived>;
     using DerivedCaster = type_caster<Derived>;
     DerivedCaster derived_caster;
     DerivedCaster derived_caster;
-protected:
     std::unique_ptr<Type> value;
     std::unique_ptr<Type> value;
 public:
 public:
     bool load(handle src, bool convert) { if (derived_caster.load(src, convert)) { value.reset(new Type(derived_caster.operator Derived&())); return true; } return false; }
     bool load(handle src, bool convert) { if (derived_caster.load(src, convert)) { value.reset(new Type(derived_caster.operator Derived&())); return true; } return false; }
@@ -182,6 +194,25 @@ public:
     template <typename _T> using cast_op_type = pybind11::detail::cast_op_type<_T>;
     template <typename _T> using cast_op_type = pybind11::detail::cast_op_type<_T>;
 };
 };
 
 
+// type_caster for special matrix types (e.g. DiagonalMatrix): load() is not supported, but we can
+// cast them into the python domain by first copying to a regular Eigen::Matrix, then casting that.
+template <typename Type>
+struct type_caster<Type, typename std::enable_if<is_eigen_base<Type>::value && !is_eigen_ref<Type>::value>::type> {
+protected:
+    using Matrix = Eigen::Matrix<typename Type::Scalar, Eigen::Dynamic, Eigen::Dynamic>;
+    using MatrixCaster = type_caster<Matrix>;
+public:
+    [[noreturn]] bool load(handle, bool) { pybind11_fail("Unable to load() into specialized EigenBase object"); }
+    static handle cast(const Type &src, return_value_policy policy, handle parent) { return MatrixCaster::cast(Matrix(src), policy, parent); }
+    static handle cast(const Type *src, return_value_policy policy, handle parent) { return MatrixCaster::cast(Matrix(*src), policy, parent); }
+
+    static PYBIND11_DESCR name() { return MatrixCaster::name(); }
+
+    [[noreturn]] operator Type*() { pybind11_fail("Loading not supported for specialized EigenBase object"); }
+    [[noreturn]] operator Type&() { pybind11_fail("Loading not supported for specialized EigenBase object"); }
+    template <typename _T> using cast_op_type = pybind11::detail::cast_op_type<_T>;
+};
+
 template<typename Type>
 template<typename Type>
 struct type_caster<Type, typename std::enable_if<is_eigen_sparse<Type>::value>::type> {
 struct type_caster<Type, typename std::enable_if<is_eigen_sparse<Type>::value>::type> {
     typedef typename Type::Scalar Scalar;
     typedef typename Type::Scalar Scalar;