/*
pybind11/eigen/matrix.h: Transparent conversion for dense and sparse Eigen matrices
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
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
#include "../numpy.h"
/* HINT: To suppress warnings originating from the Eigen headers, use -isystem.
See also:
https://stackoverflow.com/questions/2579576/i-dir-vs-isystem-dir
https://stackoverflow.com/questions/1741816/isystem-for-ms-visual-studio-c-compiler
*/
PYBIND11_WARNING_PUSH
PYBIND11_WARNING_DISABLE_MSVC(5054) // https://github.com/pybind/pybind11/pull/3741
// C5054: operator '&': deprecated between enumerations of different types
#if defined(__MINGW32__)
PYBIND11_WARNING_DISABLE_GCC("-Wmaybe-uninitialized")
#endif
#include <Eigen/Core>
#include <Eigen/SparseCore>
PYBIND11_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 matrix support in pybind11 requires Eigen >= 3.2.7");
PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
PYBIND11_WARNING_DISABLE_MSVC(4127)
// 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;
template <typename Scalar, int Flags, typename StorageIndex>
using EigenMapSparseMatrix = Eigen::Map<Eigen::SparseMatrix<Scalar, Flags, StorageIndex>>;
#else
using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE;
template <typename Scalar, int Flags, typename StorageIndex>
using EigenMapSparseMatrix = Eigen::MappedSparseMatrix<Scalar, Flags, StorageIndex>;
#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!
// 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.
// http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747
stride{EigenRowMajor ? (rstride > 0 ? rstride : 0)
: (cstride > 0 ? cstride : 0) /* outer stride */,
EigenRowMajor ? (cstride > 0 ? cstride : 0)
: (rstride > 0 ? rstride : 0) /* inner stride */},
negativestrides{rstride < 0 || cstride < 0} {}
// 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). Alternatively, if any dimension size is 0, the strides are not relevant
// (and numpy ≥ 1.23 sets the strides to 0 in that case, so we need to check explicitly).
if (negativestrides) {
return false;
}
if (rows == 0 || cols == 0) {
return true;
}
return (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)
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};
}
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 (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
= const_name("numpy.ndarray[") + npy_format_descriptor<Scalar>::name + const_name("[")
+ const_name<fixed_rows>(const_name<(size_t) rows>(), const_name("m")) + const_name(", ")
+ const_name<fixed_cols>(const_name<(size_t) cols>(), const_name("n")) + const_name("]")
+
// 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.
const_name<show_writeable>(", flags.writeable", "")
+ const_name<show_c_contiguous>(", flags.c_contiguous", "")
+ const_name<show_f_contiguous>(", flags.f_contiguous", "") + const_name("]");
};
// 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;
// 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>;
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;
}
// 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>;
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 = EigenMapSparseMatrix<Scalar,
Type::Flags &(Eigen::RowMajor | Eigen::ColMajor),
StorageIndex>(shape[0].cast<Index>(),
shape[1].cast<Index>(),
std::move(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(pybind11::make_tuple(
std::move(data), std::move(innerIndices), std::move(outerIndices)),
pybind11::make_tuple(src.rows(), src.cols()))
.release();
}
PYBIND11_TYPE_CASTER(Type,
const_name<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[",
"scipy.sparse.csc_matrix[")
+ npy_format_descriptor<Scalar>::name + const_name("]"));
};
PYBIND11_NAMESPACE_END(detail)
PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)