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Diffstat (limited to 'include/pybind11/eigen.h')
| -rw-r--r-- | include/pybind11/eigen.h | 597 |
1 files changed, 1 insertions, 596 deletions
diff --git a/include/pybind11/eigen.h b/include/pybind11/eigen.h index e8c6f633..273b9c93 100644 --- a/include/pybind11/eigen.h +++ b/include/pybind11/eigen.h @@ -9,599 +9,4 @@ #pragma once -#include "numpy.h" - -#if defined(__INTEL_COMPILER) -# pragma warning(disable: 1682) // implicit conversion of a 64-bit integral type to a smaller integral type (potential portability problem) -#elif defined(__GNUG__) || defined(__clang__) -# pragma GCC diagnostic push -# pragma GCC diagnostic ignored "-Wconversion" -# pragma GCC diagnostic ignored "-Wdeprecated-declarations" -# ifdef __clang__ -// Eigen generates a bunch of implicit-copy-constructor-is-deprecated warnings with -Wdeprecated -// under Clang, so disable that warning here: -# pragma GCC diagnostic ignored "-Wdeprecated" -# endif -# if __GNUC__ >= 7 -# pragma GCC diagnostic ignored "-Wint-in-bool-context" -# endif -#endif - -#if defined(_MSC_VER) -# pragma warning(push) -# pragma warning(disable: 4127) // warning C4127: Conditional expression is constant -# pragma warning(disable: 4996) // warning C4996: std::unary_negate is deprecated in C++17 -#endif - -#include <Eigen/Core> -#include <Eigen/SparseCore> - -// Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit -// move constructors that break things. We could detect this an explicitly copy, but an extra copy -// of matrices seems highly undesirable. -static_assert(EIGEN_VERSION_AT_LEAST(3,2,7), "Eigen support in pybind11 requires Eigen >= 3.2.7"); - -PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) - -// Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides: -using EigenDStride = Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic>; -template <typename MatrixType> using EigenDRef = Eigen::Ref<MatrixType, 0, EigenDStride>; -template <typename MatrixType> using EigenDMap = Eigen::Map<MatrixType, 0, EigenDStride>; - -PYBIND11_NAMESPACE_BEGIN(detail) - -#if EIGEN_VERSION_AT_LEAST(3,3,0) -using EigenIndex = Eigen::Index; -#else -using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE; -#endif - -// Matches Eigen::Map, Eigen::Ref, blocks, etc: -template <typename T> using is_eigen_dense_map = all_of<is_template_base_of<Eigen::DenseBase, T>, std::is_base_of<Eigen::MapBase<T, Eigen::ReadOnlyAccessors>, T>>; -template <typename T> using is_eigen_mutable_map = std::is_base_of<Eigen::MapBase<T, Eigen::WriteAccessors>, T>; -template <typename T> using is_eigen_dense_plain = all_of<negation<is_eigen_dense_map<T>>, is_template_base_of<Eigen::PlainObjectBase, T>>; -template <typename T> using is_eigen_sparse = is_template_base_of<Eigen::SparseMatrixBase, T>; -// 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> using is_eigen_other = all_of< - is_template_base_of<Eigen::EigenBase, T>, - negation<any_of<is_eigen_dense_map<T>, is_eigen_dense_plain<T>, is_eigen_sparse<T>>> ->; - -// Captures numpy/eigen conformability status (returned by EigenProps::conformable()): -template <bool EigenRowMajor> struct EigenConformable { - bool conformable = false; - EigenIndex rows = 0, cols = 0; - EigenDStride stride{0, 0}; // Only valid if negativestrides is false! - bool negativestrides = false; // If true, do not use stride! - - EigenConformable(bool fits = false) : conformable{fits} {} - // Matrix type: - EigenConformable(EigenIndex r, EigenIndex c, - EigenIndex rstride, EigenIndex cstride) : - conformable{true}, rows{r}, cols{c} { - // TODO: when Eigen bug #747 is fixed, remove the tests for non-negativity. http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747 - if (rstride < 0 || cstride < 0) { - negativestrides = true; - } else { - stride = {EigenRowMajor ? rstride : cstride /* outer stride */, - EigenRowMajor ? cstride : rstride /* inner stride */ }; - } - } - // Vector type: - EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride) - : EigenConformable(r, c, r == 1 ? c*stride : stride, c == 1 ? r : r*stride) {} - - template <typename props> bool stride_compatible() const { - // To have compatible strides, we need (on both dimensions) one of fully dynamic strides, - // matching strides, or a dimension size of 1 (in which case the stride value is irrelevant) - return - !negativestrides && - (props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner() || - (EigenRowMajor ? cols : rows) == 1) && - (props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer() || - (EigenRowMajor ? rows : cols) == 1); - } - operator bool() const { return conformable; } -}; - -template <typename Type> struct eigen_extract_stride { using type = Type; }; -template <typename PlainObjectType, int MapOptions, typename StrideType> -struct eigen_extract_stride<Eigen::Map<PlainObjectType, MapOptions, StrideType>> { using type = StrideType; }; -template <typename PlainObjectType, int Options, typename StrideType> -struct eigen_extract_stride<Eigen::Ref<PlainObjectType, Options, StrideType>> { using type = StrideType; }; - -// Helper struct for extracting information from an Eigen type -template <typename Type_> struct EigenProps { - using Type = Type_; - using Scalar = typename Type::Scalar; - using StrideType = typename eigen_extract_stride<Type>::type; - static constexpr EigenIndex - rows = Type::RowsAtCompileTime, - cols = Type::ColsAtCompileTime, - size = Type::SizeAtCompileTime; - static constexpr bool - row_major = Type::IsRowMajor, - vector = Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1 - fixed_rows = rows != Eigen::Dynamic, - fixed_cols = cols != Eigen::Dynamic, - fixed = size != Eigen::Dynamic, // Fully-fixed size - dynamic = !fixed_rows && !fixed_cols; // Fully-dynamic size - - template <EigenIndex i, EigenIndex ifzero> using if_zero = std::integral_constant<EigenIndex, i == 0 ? ifzero : i>; - static constexpr EigenIndex inner_stride = if_zero<StrideType::InnerStrideAtCompileTime, 1>::value, - outer_stride = if_zero<StrideType::OuterStrideAtCompileTime, - vector ? size : row_major ? cols : rows>::value; - static constexpr bool dynamic_stride = inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic; - static constexpr bool requires_row_major = !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1; - static constexpr bool requires_col_major = !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1; - - // Takes an input array and determines whether we can make it fit into the Eigen type. If - // the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector - // (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type). - static EigenConformable<row_major> conformable(const array &a) { - const auto dims = a.ndim(); - if (dims < 1 || dims > 2) - return false; - - if (dims == 2) { // Matrix type: require exact match (or dynamic) - - EigenIndex - np_rows = a.shape(0), - np_cols = a.shape(1), - np_rstride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar)), - np_cstride = a.strides(1) / static_cast<ssize_t>(sizeof(Scalar)); - if ((fixed_rows && np_rows != rows) || (fixed_cols && np_cols != cols)) - return false; - - return {np_rows, np_cols, np_rstride, np_cstride}; - } - - // Otherwise we're storing an n-vector. Only one of the strides will be used, but whichever - // is used, we want the (single) numpy stride value. - const EigenIndex n = a.shape(0), - stride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar)); - - if (vector) { // Eigen type is a compile-time vector - if (fixed && size != n) - return false; // Vector size mismatch - return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride}; - } - else if (fixed) { - // The type has a fixed size, but is not a vector: abort - return false; - } - else if (fixed_cols) { - // Since this isn't a vector, cols must be != 1. We allow this only if it exactly - // equals the number of elements (rows is Dynamic, and so 1 row is allowed). - if (cols != n) return false; - return {1, n, stride}; - } - else { - // Otherwise it's either fully dynamic, or column dynamic; both become a column vector - if (fixed_rows && rows != n) return false; - return {n, 1, stride}; - } - } - - static constexpr bool show_writeable = is_eigen_dense_map<Type>::value && is_eigen_mutable_map<Type>::value; - static constexpr bool show_order = is_eigen_dense_map<Type>::value; - static constexpr bool show_c_contiguous = show_order && requires_row_major; - static constexpr bool show_f_contiguous = !show_c_contiguous && show_order && requires_col_major; - - static constexpr auto descriptor = - _("numpy.ndarray[") + npy_format_descriptor<Scalar>::name + - _("[") + _<fixed_rows>(_<(size_t) rows>(), _("m")) + - _(", ") + _<fixed_cols>(_<(size_t) cols>(), _("n")) + - _("]") + - // For a reference type (e.g. Ref<MatrixXd>) we have other constraints that might need to be - // satisfied: writeable=True (for a mutable reference), and, depending on the map's stride - // options, possibly f_contiguous or c_contiguous. We include them in the descriptor output - // to provide some hint as to why a TypeError is occurring (otherwise it can be confusing to - // see that a function accepts a 'numpy.ndarray[float64[3,2]]' and an error message that you - // *gave* a numpy.ndarray of the right type and dimensions. - _<show_writeable>(", flags.writeable", "") + - _<show_c_contiguous>(", flags.c_contiguous", "") + - _<show_f_contiguous>(", flags.f_contiguous", "") + - _("]"); -}; - -// Casts an Eigen type to numpy array. If given a base, the numpy array references the src data, -// otherwise it'll make a copy. writeable lets you turn off the writeable flag for the array. -template <typename props> handle eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) { - constexpr ssize_t elem_size = sizeof(typename props::Scalar); - array a; - if (props::vector) - a = array({ src.size() }, { elem_size * src.innerStride() }, src.data(), base); - else - a = array({ src.rows(), src.cols() }, { elem_size * src.rowStride(), elem_size * src.colStride() }, - src.data(), base); - - if (!writeable) - array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_; - - return a.release(); -} - -// Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that -// reference the Eigen object's data with `base` as the python-registered base class (if omitted, -// the base will be set to None, and lifetime management is up to the caller). The numpy array is -// non-writeable if the given type is const. -template <typename props, typename Type> -handle eigen_ref_array(Type &src, handle parent = none()) { - // none here is to get past array's should-we-copy detection, which currently always - // copies when there is no base. Setting the base to None should be harmless. - return eigen_array_cast<props>(src, parent, !std::is_const<Type>::value); -} - -// Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a numpy -// array that references the encapsulated data with a python-side reference to the capsule to tie -// its destruction to that of any dependent python objects. Const-ness is determined by whether or -// not the Type of the pointer given is const. -template <typename props, typename Type, typename = enable_if_t<is_eigen_dense_plain<Type>::value>> -handle eigen_encapsulate(Type *src) { - capsule base(src, [](void *o) { delete static_cast<Type *>(o); }); - return eigen_ref_array<props>(*src, base); -} - -// Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense -// types. -template<typename Type> -struct type_caster<Type, enable_if_t<is_eigen_dense_plain<Type>::value>> { - using Scalar = typename Type::Scalar; - using props = EigenProps<Type>; - - bool load(handle src, bool convert) { - // If we're in no-convert mode, only load if given an array of the correct type - if (!convert && !isinstance<array_t<Scalar>>(src)) - return false; - - // Coerce into an array, but don't do type conversion yet; the copy below handles it. - auto buf = array::ensure(src); - - if (!buf) - return false; - - auto dims = buf.ndim(); - if (dims < 1 || dims > 2) - return false; - - auto fits = props::conformable(buf); - if (!fits) - return false; - - // Allocate the new type, then build a numpy reference into it - value = Type(fits.rows, fits.cols); - auto ref = reinterpret_steal<array>(eigen_ref_array<props>(value)); - if (dims == 1) ref = ref.squeeze(); - else if (ref.ndim() == 1) buf = buf.squeeze(); - - int result = detail::npy_api::get().PyArray_CopyInto_(ref.ptr(), buf.ptr()); - - if (result < 0) { // Copy failed! - PyErr_Clear(); - return false; - } - - return true; - } - -private: - - // Cast implementation - template <typename CType> - static handle cast_impl(CType *src, return_value_policy policy, handle parent) { - switch (policy) { - case return_value_policy::take_ownership: - case return_value_policy::automatic: - return eigen_encapsulate<props>(src); - case return_value_policy::move: - return eigen_encapsulate<props>(new CType(std::move(*src))); - case return_value_policy::copy: - return eigen_array_cast<props>(*src); - case return_value_policy::reference: - case return_value_policy::automatic_reference: - return eigen_ref_array<props>(*src); - case return_value_policy::reference_internal: - return eigen_ref_array<props>(*src, parent); - default: - throw cast_error("unhandled return_value_policy: should not happen!"); - }; - } - -public: - - // Normal returned non-reference, non-const value: - static handle cast(Type &&src, return_value_policy /* policy */, handle parent) { - return cast_impl(&src, return_value_policy::move, parent); - } - // If you return a non-reference const, we mark the numpy array readonly: - static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) { - return cast_impl(&src, return_value_policy::move, parent); - } - // lvalue reference return; default (automatic) becomes copy - static handle cast(Type &src, return_value_policy policy, handle parent) { - if (policy == return_value_policy::automatic || policy == return_value_policy::automatic_reference) - policy = return_value_policy::copy; - return cast_impl(&src, policy, parent); - } - // const lvalue reference return; default (automatic) becomes copy - static handle cast(const Type &src, return_value_policy policy, handle parent) { - if (policy == return_value_policy::automatic || policy == return_value_policy::automatic_reference) - policy = return_value_policy::copy; - return cast(&src, policy, parent); - } - // non-const pointer return - static handle cast(Type *src, return_value_policy policy, handle parent) { - return cast_impl(src, policy, parent); - } - // const pointer return - static handle cast(const Type *src, return_value_policy policy, handle parent) { - return cast_impl(src, policy, parent); - } - - static constexpr auto name = props::descriptor; - - operator Type*() { return &value; } - operator Type&() { return value; } - operator Type&&() && { return std::move(value); } - template <typename T> using cast_op_type = movable_cast_op_type<T>; - -private: - Type value; -}; - -// Base class for casting reference/map/block/etc. objects back to python. -template <typename MapType> struct eigen_map_caster { -private: - using props = EigenProps<MapType>; - -public: - - // Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has - // to stay around), but we'll allow it under the assumption that you know what you're doing (and - // have an appropriate keep_alive in place). We return a numpy array pointing directly at the - // ref's data (The numpy array ends up read-only if the ref was to a const matrix type.) Note - // that this means you need to ensure you don't destroy the object in some other way (e.g. with - // an appropriate keep_alive, or with a reference to a statically allocated matrix). - static handle cast(const MapType &src, return_value_policy policy, handle parent) { - switch (policy) { - case return_value_policy::copy: - return eigen_array_cast<props>(src); - case return_value_policy::reference_internal: - return eigen_array_cast<props>(src, parent, is_eigen_mutable_map<MapType>::value); - case return_value_policy::reference: - case return_value_policy::automatic: - case return_value_policy::automatic_reference: - return eigen_array_cast<props>(src, none(), is_eigen_mutable_map<MapType>::value); - default: - // move, take_ownership don't make any sense for a ref/map: - pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type"); - } - } - - static constexpr auto name = props::descriptor; - - // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return - // types but not bound arguments). We still provide them (with an explicitly delete) so that - // you end up here if you try anyway. - bool load(handle, bool) = delete; - operator MapType() = delete; - template <typename> using cast_op_type = MapType; -}; - -// We can return any map-like object (but can only load Refs, specialized next): -template <typename Type> struct type_caster<Type, enable_if_t<is_eigen_dense_map<Type>::value>> - : eigen_map_caster<Type> {}; - -// Loader for Ref<...> arguments. See the documentation for info on how to make this work without -// copying (it requires some extra effort in many cases). -template <typename PlainObjectType, typename StrideType> -struct type_caster< - Eigen::Ref<PlainObjectType, 0, StrideType>, - enable_if_t<is_eigen_dense_map<Eigen::Ref<PlainObjectType, 0, StrideType>>::value> -> : public eigen_map_caster<Eigen::Ref<PlainObjectType, 0, StrideType>> { -private: - using Type = Eigen::Ref<PlainObjectType, 0, StrideType>; - using props = EigenProps<Type>; - using Scalar = typename props::Scalar; - using MapType = Eigen::Map<PlainObjectType, 0, StrideType>; - using Array = array_t<Scalar, array::forcecast | - ((props::row_major ? props::inner_stride : props::outer_stride) == 1 ? array::c_style : - (props::row_major ? props::outer_stride : props::inner_stride) == 1 ? array::f_style : 0)>; - static constexpr bool need_writeable = is_eigen_mutable_map<Type>::value; - // Delay construction (these have no default constructor) - std::unique_ptr<MapType> map; - std::unique_ptr<Type> ref; - // Our array. When possible, this is just a numpy array pointing to the source data, but - // sometimes we can't avoid copying (e.g. input is not a numpy array at all, has an incompatible - // layout, or is an array of a type that needs to be converted). Using a numpy temporary - // (rather than an Eigen temporary) saves an extra copy when we need both type conversion and - // storage order conversion. (Note that we refuse to use this temporary copy when loading an - // argument for a Ref<M> with M non-const, i.e. a read-write reference). - Array copy_or_ref; -public: - bool load(handle src, bool convert) { - // First check whether what we have is already an array of the right type. If not, we can't - // avoid a copy (because the copy is also going to do type conversion). - bool need_copy = !isinstance<Array>(src); - - EigenConformable<props::row_major> fits; - if (!need_copy) { - // We don't need a converting copy, but we also need to check whether the strides are - // compatible with the Ref's stride requirements - auto aref = reinterpret_borrow<Array>(src); - - if (aref && (!need_writeable || aref.writeable())) { - fits = props::conformable(aref); - if (!fits) return false; // Incompatible dimensions - if (!fits.template stride_compatible<props>()) - need_copy = true; - else - copy_or_ref = std::move(aref); - } - else { - need_copy = true; - } - } - - if (need_copy) { - // We need to copy: If we need a mutable reference, or we're not supposed to convert - // (either because we're in the no-convert overload pass, or because we're explicitly - // instructed not to copy (via `py::arg().noconvert()`) we have to fail loading. - if (!convert || need_writeable) return false; - - Array copy = Array::ensure(src); - if (!copy) return false; - fits = props::conformable(copy); - if (!fits || !fits.template stride_compatible<props>()) - return false; - copy_or_ref = std::move(copy); - loader_life_support::add_patient(copy_or_ref); - } - - ref.reset(); - map.reset(new MapType(data(copy_or_ref), fits.rows, fits.cols, make_stride(fits.stride.outer(), fits.stride.inner()))); - ref.reset(new Type(*map)); - - return true; - } - - operator Type*() { return ref.get(); } - operator Type&() { return *ref; } - template <typename _T> using cast_op_type = pybind11::detail::cast_op_type<_T>; - -private: - template <typename T = Type, enable_if_t<is_eigen_mutable_map<T>::value, int> = 0> - Scalar *data(Array &a) { return a.mutable_data(); } - - template <typename T = Type, enable_if_t<!is_eigen_mutable_map<T>::value, int> = 0> - const Scalar *data(Array &a) { return a.data(); } - - // Attempt to figure out a constructor of `Stride` that will work. - // If both strides are fixed, use a default constructor: - template <typename S> using stride_ctor_default = bool_constant< - S::InnerStrideAtCompileTime != Eigen::Dynamic && S::OuterStrideAtCompileTime != Eigen::Dynamic && - std::is_default_constructible<S>::value>; - // Otherwise, if there is a two-index constructor, assume it is (outer,inner) like - // Eigen::Stride, and use it: - template <typename S> using stride_ctor_dual = bool_constant< - !stride_ctor_default<S>::value && std::is_constructible<S, EigenIndex, EigenIndex>::value>; - // Otherwise, if there is a one-index constructor, and just one of the strides is dynamic, use - // it (passing whichever stride is dynamic). - template <typename S> using stride_ctor_outer = bool_constant< - !any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value && - S::OuterStrideAtCompileTime == Eigen::Dynamic && S::InnerStrideAtCompileTime != Eigen::Dynamic && - std::is_constructible<S, EigenIndex>::value>; - template <typename S> using stride_ctor_inner = bool_constant< - !any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value && - S::InnerStrideAtCompileTime == Eigen::Dynamic && S::OuterStrideAtCompileTime != Eigen::Dynamic && - std::is_constructible<S, EigenIndex>::value>; - - template <typename S = StrideType, enable_if_t<stride_ctor_default<S>::value, int> = 0> - static S make_stride(EigenIndex, EigenIndex) { return S(); } - template <typename S = StrideType, enable_if_t<stride_ctor_dual<S>::value, int> = 0> - static S make_stride(EigenIndex outer, EigenIndex inner) { return S(outer, inner); } - template <typename S = StrideType, enable_if_t<stride_ctor_outer<S>::value, int> = 0> - static S make_stride(EigenIndex outer, EigenIndex) { return S(outer); } - template <typename S = StrideType, enable_if_t<stride_ctor_inner<S>::value, int> = 0> - static S make_stride(EigenIndex, EigenIndex inner) { return S(inner); } - -}; - -// type_caster for special matrix types (e.g. DiagonalMatrix), which are EigenBase, but not -// EigenDense (i.e. they don't have a data(), at least not with the usual matrix layout). -// 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, enable_if_t<is_eigen_other<Type>::value>> { -protected: - using Matrix = Eigen::Matrix<typename Type::Scalar, Type::RowsAtCompileTime, Type::ColsAtCompileTime>; - using props = EigenProps<Matrix>; -public: - static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) { - handle h = eigen_encapsulate<props>(new Matrix(src)); - return h; - } - static handle cast(const Type *src, return_value_policy policy, handle parent) { return cast(*src, policy, parent); } - - static constexpr auto name = props::descriptor; - - // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return - // types but not bound arguments). We still provide them (with an explicitly delete) so that - // you end up here if you try anyway. - bool load(handle, bool) = delete; - operator Type() = delete; - template <typename> using cast_op_type = Type; -}; - -template<typename Type> -struct type_caster<Type, enable_if_t<is_eigen_sparse<Type>::value>> { - using Scalar = typename Type::Scalar; - using StorageIndex = remove_reference_t<decltype(*std::declval<Type>().outerIndexPtr())>; - using Index = typename Type::Index; - static constexpr bool rowMajor = Type::IsRowMajor; - - bool load(handle src, bool) { - if (!src) - return false; - - auto obj = reinterpret_borrow<object>(src); - object sparse_module = module_::import("scipy.sparse"); - object matrix_type = sparse_module.attr( - rowMajor ? "csr_matrix" : "csc_matrix"); - - if (!type::handle_of(obj).is(matrix_type)) { - try { - obj = matrix_type(obj); - } catch (const error_already_set &) { - return false; - } - } - - auto values = array_t<Scalar>((object) obj.attr("data")); - auto innerIndices = array_t<StorageIndex>((object) obj.attr("indices")); - auto outerIndices = array_t<StorageIndex>((object) obj.attr("indptr")); - auto shape = pybind11::tuple((pybind11::object) obj.attr("shape")); - auto nnz = obj.attr("nnz").cast<Index>(); - - if (!values || !innerIndices || !outerIndices) - return false; - - value = Eigen::MappedSparseMatrix<Scalar, Type::Flags, StorageIndex>( - shape[0].cast<Index>(), shape[1].cast<Index>(), nnz, - outerIndices.mutable_data(), innerIndices.mutable_data(), values.mutable_data()); - - return true; - } - - static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) { - const_cast<Type&>(src).makeCompressed(); - - object matrix_type = module_::import("scipy.sparse").attr( - rowMajor ? "csr_matrix" : "csc_matrix"); - - array data(src.nonZeros(), src.valuePtr()); - array outerIndices((rowMajor ? src.rows() : src.cols()) + 1, src.outerIndexPtr()); - array innerIndices(src.nonZeros(), src.innerIndexPtr()); - - return matrix_type( - std::make_tuple(data, innerIndices, outerIndices), - std::make_pair(src.rows(), src.cols()) - ).release(); - } - - PYBIND11_TYPE_CASTER(Type, _<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[", "scipy.sparse.csc_matrix[") - + npy_format_descriptor<Scalar>::name + _("]")); -}; - -PYBIND11_NAMESPACE_END(detail) -PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE) - -#if defined(__GNUG__) || defined(__clang__) -# pragma GCC diagnostic pop -#elif defined(_MSC_VER) -# pragma warning(pop) -#endif +#include "eigen/matrix.h" |