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pybind11/eigen.h: Transparent conversion for dense and sparse Eigen matrices
Copyright (c) 2016 Wenzel Jakob <>
All rights reserved. Use of this source code is governed by a
BSD-style license that can be found in the LICENSE file.
#pragma once
/* HINT: To suppress warnings originating from the Eigen headers, use -isystem.
See also:
#include "numpy.h"
// The C4127 suppression was introduced for Eigen 3.4.0. In theory we could
// make it version specific, or even remove it later, but considering that
// 1. C4127 is generally far more distracting than useful for modern template code, and
// 2. we definitely want to ignore any MSVC warnings originating from Eigen code,
// it is probably best to keep this around indefinitely.
#if defined(_MSC_VER)
# pragma warning(push)
# pragma warning(disable: 4127) // C4127: conditional expression is constant
#include <Eigen/Core>
#include <Eigen/SparseCore>
#if defined(_MSC_VER)
# pragma warning(pop)
// 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");
// 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>;
using EigenIndex = Eigen::Index;
// 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!
// NOLINTNEXTLINE(google-explicit-constructor)
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.
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)
!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);
// NOLINTNEXTLINE(google-explicit-constructor)
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)
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 ((PYBIND11_SILENCE_MSVC_C4127(fixed_rows) && np_rows != rows) ||
(PYBIND11_SILENCE_MSVC_C4127(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 (PYBIND11_SILENCE_MSVC_C4127(fixed) && size != n)
return false; // Vector size mismatch
return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride};
if (fixed) {
// The type has a fixed size, but is not a vector: abort
return false;
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};
} // Otherwise it's either fully dynamic, or column dynamic; both become a column vector
if (PYBIND11_SILENCE_MSVC_C4127(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() },, base);
a = array({ src.rows(), src.cols() }, { elem_size * src.rowStride(), elem_size * src.colStride() },, 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!
return false;
return true;
// 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);
throw cast_error("unhandled return_value_policy: should not happen!");
// 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;
// NOLINTNEXTLINE(google-explicit-constructor)
operator Type*() { return &value; }
// NOLINTNEXTLINE(google-explicit-constructor)
operator Type&() { return value; }
// NOLINTNEXTLINE(google-explicit-constructor)
operator Type&&() && { return std::move(value); }
template <typename T> using cast_op_type = movable_cast_op_type<T>;
Type value;
// Base class for casting reference/map/block/etc. objects back to python.
template <typename MapType> struct eigen_map_caster {
using props = EigenProps<MapType>;
// 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);
// 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>> {
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;
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;
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);
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;
// NOLINTNEXTLINE(google-explicit-constructor)
operator Type*() { return ref.get(); }
// NOLINTNEXTLINE(google-explicit-constructor)
operator Type&() { return *ref; }
template <typename _T> using cast_op_type = pybind11::detail::cast_op_type<_T>;
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; }
// 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 &&
// 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>> {
using Matrix = Eigen::Matrix<typename Type::Scalar, Type::RowsAtCompileTime, Type::ColsAtCompileTime>;
using props = EigenProps<Matrix>;
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 & (Eigen::RowMajor | Eigen::ColMajor),
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 */) {
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())
PYBIND11_TYPE_CASTER(Type, _<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[", "scipy.sparse.csc_matrix[")
+ npy_format_descriptor<Scalar>::name + _("]"));