eigen.h revision 12037:d28054ac6ec9
1/* 2 pybind11/eigen.h: Transparent conversion for dense and sparse Eigen matrices 3 4 Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch> 5 6 All rights reserved. Use of this source code is governed by a 7 BSD-style license that can be found in the LICENSE file. 8*/ 9 10#pragma once 11 12#include "numpy.h" 13 14#if defined(__INTEL_COMPILER) 15# pragma warning(disable: 1682) // implicit conversion of a 64-bit integral type to a smaller integral type (potential portability problem) 16#elif defined(__GNUG__) || defined(__clang__) 17# pragma GCC diagnostic push 18# pragma GCC diagnostic ignored "-Wconversion" 19# pragma GCC diagnostic ignored "-Wdeprecated-declarations" 20# if __GNUC__ >= 7 21# pragma GCC diagnostic ignored "-Wint-in-bool-context" 22# endif 23#endif 24 25#include <Eigen/Core> 26#include <Eigen/SparseCore> 27 28#if defined(_MSC_VER) 29# pragma warning(push) 30# pragma warning(disable: 4127) // warning C4127: Conditional expression is constant 31#endif 32 33// Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit 34// move constructors that break things. We could detect this an explicitly copy, but an extra copy 35// of matrices seems highly undesirable. 36static_assert(EIGEN_VERSION_AT_LEAST(3,2,7), "Eigen support in pybind11 requires Eigen >= 3.2.7"); 37 38NAMESPACE_BEGIN(pybind11) 39 40// Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides: 41using EigenDStride = Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic>; 42template <typename MatrixType> using EigenDRef = Eigen::Ref<MatrixType, 0, EigenDStride>; 43template <typename MatrixType> using EigenDMap = Eigen::Map<MatrixType, 0, EigenDStride>; 44 45NAMESPACE_BEGIN(detail) 46 47#if EIGEN_VERSION_AT_LEAST(3,3,0) 48using EigenIndex = Eigen::Index; 49#else 50using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE; 51#endif 52 53// Matches Eigen::Map, Eigen::Ref, blocks, etc: 54template <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>>; 55template <typename T> using is_eigen_mutable_map = std::is_base_of<Eigen::MapBase<T, Eigen::WriteAccessors>, T>; 56template <typename T> using is_eigen_dense_plain = all_of<negation<is_eigen_dense_map<T>>, is_template_base_of<Eigen::PlainObjectBase, T>>; 57template <typename T> using is_eigen_sparse = is_template_base_of<Eigen::SparseMatrixBase, T>; 58// Test for objects inheriting from EigenBase<Derived> that aren't captured by the above. This 59// basically covers anything that can be assigned to a dense matrix but that don't have a typical 60// matrix data layout that can be copied from their .data(). For example, DiagonalMatrix and 61// SelfAdjointView fall into this category. 62template <typename T> using is_eigen_other = all_of< 63 is_template_base_of<Eigen::EigenBase, T>, 64 negation<any_of<is_eigen_dense_map<T>, is_eigen_dense_plain<T>, is_eigen_sparse<T>>> 65>; 66 67// Captures numpy/eigen conformability status (returned by EigenProps::conformable()): 68template <bool EigenRowMajor> struct EigenConformable { 69 bool conformable = false; 70 EigenIndex rows = 0, cols = 0; 71 EigenDStride stride{0, 0}; 72 73 EigenConformable(bool fits = false) : conformable{fits} {} 74 // Matrix type: 75 EigenConformable(EigenIndex r, EigenIndex c, 76 EigenIndex rstride, EigenIndex cstride) : 77 conformable{true}, rows{r}, cols{c}, 78 stride(EigenRowMajor ? rstride : cstride /* outer stride */, 79 EigenRowMajor ? cstride : rstride /* inner stride */) 80 {} 81 // Vector type: 82 EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride) 83 : EigenConformable(r, c, r == 1 ? c*stride : stride, c == 1 ? r : r*stride) {} 84 85 template <typename props> bool stride_compatible() const { 86 // To have compatible strides, we need (on both dimensions) one of fully dynamic strides, 87 // matching strides, or a dimension size of 1 (in which case the stride value is irrelevant) 88 return 89 (props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner() || 90 (EigenRowMajor ? cols : rows) == 1) && 91 (props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer() || 92 (EigenRowMajor ? rows : cols) == 1); 93 } 94 operator bool() const { return conformable; } 95}; 96 97template <typename Type> struct eigen_extract_stride { using type = Type; }; 98template <typename PlainObjectType, int MapOptions, typename StrideType> 99struct eigen_extract_stride<Eigen::Map<PlainObjectType, MapOptions, StrideType>> { using type = StrideType; }; 100template <typename PlainObjectType, int Options, typename StrideType> 101struct eigen_extract_stride<Eigen::Ref<PlainObjectType, Options, StrideType>> { using type = StrideType; }; 102 103// Helper struct for extracting information from an Eigen type 104template <typename Type_> struct EigenProps { 105 using Type = Type_; 106 using Scalar = typename Type::Scalar; 107 using StrideType = typename eigen_extract_stride<Type>::type; 108 static constexpr EigenIndex 109 rows = Type::RowsAtCompileTime, 110 cols = Type::ColsAtCompileTime, 111 size = Type::SizeAtCompileTime; 112 static constexpr bool 113 row_major = Type::IsRowMajor, 114 vector = Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1 115 fixed_rows = rows != Eigen::Dynamic, 116 fixed_cols = cols != Eigen::Dynamic, 117 fixed = size != Eigen::Dynamic, // Fully-fixed size 118 dynamic = !fixed_rows && !fixed_cols; // Fully-dynamic size 119 120 template <EigenIndex i, EigenIndex ifzero> using if_zero = std::integral_constant<EigenIndex, i == 0 ? ifzero : i>; 121 static constexpr EigenIndex inner_stride = if_zero<StrideType::InnerStrideAtCompileTime, 1>::value, 122 outer_stride = if_zero<StrideType::OuterStrideAtCompileTime, 123 vector ? size : row_major ? cols : rows>::value; 124 static constexpr bool dynamic_stride = inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic; 125 static constexpr bool requires_row_major = !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1; 126 static constexpr bool requires_col_major = !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1; 127 128 // Takes an input array and determines whether we can make it fit into the Eigen type. If 129 // the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector 130 // (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type). 131 static EigenConformable<row_major> conformable(const array &a) { 132 const auto dims = a.ndim(); 133 if (dims < 1 || dims > 2) 134 return false; 135 136 if (dims == 2) { // Matrix type: require exact match (or dynamic) 137 138 EigenIndex 139 np_rows = a.shape(0), 140 np_cols = a.shape(1), 141 np_rstride = a.strides(0) / sizeof(Scalar), 142 np_cstride = a.strides(1) / sizeof(Scalar); 143 if ((fixed_rows && np_rows != rows) || (fixed_cols && np_cols != cols)) 144 return false; 145 146 return {np_rows, np_cols, np_rstride, np_cstride}; 147 } 148 149 // Otherwise we're storing an n-vector. Only one of the strides will be used, but whichever 150 // is used, we want the (single) numpy stride value. 151 const EigenIndex n = a.shape(0), 152 stride = a.strides(0) / sizeof(Scalar); 153 154 if (vector) { // Eigen type is a compile-time vector 155 if (fixed && size != n) 156 return false; // Vector size mismatch 157 return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride}; 158 } 159 else if (fixed) { 160 // The type has a fixed size, but is not a vector: abort 161 return false; 162 } 163 else if (fixed_cols) { 164 // Since this isn't a vector, cols must be != 1. We allow this only if it exactly 165 // equals the number of elements (rows is Dynamic, and so 1 row is allowed). 166 if (cols != n) return false; 167 return {1, n, stride}; 168 } 169 else { 170 // Otherwise it's either fully dynamic, or column dynamic; both become a column vector 171 if (fixed_rows && rows != n) return false; 172 return {n, 1, stride}; 173 } 174 } 175 176 static PYBIND11_DESCR descriptor() { 177 constexpr bool show_writeable = is_eigen_dense_map<Type>::value && is_eigen_mutable_map<Type>::value; 178 constexpr bool show_order = is_eigen_dense_map<Type>::value; 179 constexpr bool show_c_contiguous = show_order && requires_row_major; 180 constexpr bool show_f_contiguous = !show_c_contiguous && show_order && requires_col_major; 181 182 return _("numpy.ndarray[") + npy_format_descriptor<Scalar>::name() + 183 _("[") + _<fixed_rows>(_<(size_t) rows>(), _("m")) + 184 _(", ") + _<fixed_cols>(_<(size_t) cols>(), _("n")) + 185 _("]") + 186 // For a reference type (e.g. Ref<MatrixXd>) we have other constraints that might need to be 187 // satisfied: writeable=True (for a mutable reference), and, depending on the map's stride 188 // options, possibly f_contiguous or c_contiguous. We include them in the descriptor output 189 // to provide some hint as to why a TypeError is occurring (otherwise it can be confusing to 190 // see that a function accepts a 'numpy.ndarray[float64[3,2]]' and an error message that you 191 // *gave* a numpy.ndarray of the right type and dimensions. 192 _<show_writeable>(", flags.writeable", "") + 193 _<show_c_contiguous>(", flags.c_contiguous", "") + 194 _<show_f_contiguous>(", flags.f_contiguous", "") + 195 _("]"); 196 } 197}; 198 199// Casts an Eigen type to numpy array. If given a base, the numpy array references the src data, 200// otherwise it'll make a copy. writeable lets you turn off the writeable flag for the array. 201template <typename props> handle eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) { 202 constexpr size_t elem_size = sizeof(typename props::Scalar); 203 std::vector<size_t> shape, strides; 204 if (props::vector) { 205 shape.push_back(src.size()); 206 strides.push_back(elem_size * src.innerStride()); 207 } 208 else { 209 shape.push_back(src.rows()); 210 shape.push_back(src.cols()); 211 strides.push_back(elem_size * src.rowStride()); 212 strides.push_back(elem_size * src.colStride()); 213 } 214 array a(std::move(shape), std::move(strides), src.data(), base); 215 if (!writeable) 216 array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_; 217 218 return a.release(); 219} 220 221// Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that 222// reference the Eigen object's data with `base` as the python-registered base class (if omitted, 223// the base will be set to None, and lifetime management is up to the caller). The numpy array is 224// non-writeable if the given type is const. 225template <typename props, typename Type> 226handle eigen_ref_array(Type &src, handle parent = none()) { 227 // none here is to get past array's should-we-copy detection, which currently always 228 // copies when there is no base. Setting the base to None should be harmless. 229 return eigen_array_cast<props>(src, parent, !std::is_const<Type>::value); 230} 231 232// Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a numpy 233// array that references the encapsulated data with a python-side reference to the capsule to tie 234// its destruction to that of any dependent python objects. Const-ness is determined by whether or 235// not the Type of the pointer given is const. 236template <typename props, typename Type, typename = enable_if_t<is_eigen_dense_plain<Type>::value>> 237handle eigen_encapsulate(Type *src) { 238 capsule base(src, [](void *o) { delete static_cast<Type *>(o); }); 239 return eigen_ref_array<props>(*src, base); 240} 241 242// Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense 243// types. 244template<typename Type> 245struct type_caster<Type, enable_if_t<is_eigen_dense_plain<Type>::value>> { 246 using Scalar = typename Type::Scalar; 247 using props = EigenProps<Type>; 248 249 bool load(handle src, bool) { 250 auto buf = array_t<Scalar>::ensure(src); 251 if (!buf) 252 return false; 253 254 auto dims = buf.ndim(); 255 if (dims < 1 || dims > 2) 256 return false; 257 258 auto fits = props::conformable(buf); 259 if (!fits) 260 return false; // Non-comformable vector/matrix types 261 262 value = Eigen::Map<const Type, 0, EigenDStride>(buf.data(), fits.rows, fits.cols, fits.stride); 263 264 return true; 265 } 266 267private: 268 269 // Cast implementation 270 template <typename CType> 271 static handle cast_impl(CType *src, return_value_policy policy, handle parent) { 272 switch (policy) { 273 case return_value_policy::take_ownership: 274 case return_value_policy::automatic: 275 return eigen_encapsulate<props>(src); 276 case return_value_policy::move: 277 return eigen_encapsulate<props>(new CType(std::move(*src))); 278 case return_value_policy::copy: 279 return eigen_array_cast<props>(*src); 280 case return_value_policy::reference: 281 case return_value_policy::automatic_reference: 282 return eigen_ref_array<props>(*src); 283 case return_value_policy::reference_internal: 284 return eigen_ref_array<props>(*src, parent); 285 default: 286 throw cast_error("unhandled return_value_policy: should not happen!"); 287 }; 288 } 289 290public: 291 292 // Normal returned non-reference, non-const value: 293 static handle cast(Type &&src, return_value_policy /* policy */, handle parent) { 294 return cast_impl(&src, return_value_policy::move, parent); 295 } 296 // If you return a non-reference const, we mark the numpy array readonly: 297 static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) { 298 return cast_impl(&src, return_value_policy::move, parent); 299 } 300 // lvalue reference return; default (automatic) becomes copy 301 static handle cast(Type &src, return_value_policy policy, handle parent) { 302 if (policy == return_value_policy::automatic || policy == return_value_policy::automatic_reference) 303 policy = return_value_policy::copy; 304 return cast_impl(&src, policy, parent); 305 } 306 // const lvalue reference return; default (automatic) becomes copy 307 static handle cast(const Type &src, return_value_policy policy, handle parent) { 308 if (policy == return_value_policy::automatic || policy == return_value_policy::automatic_reference) 309 policy = return_value_policy::copy; 310 return cast(&src, policy, parent); 311 } 312 // non-const pointer return 313 static handle cast(Type *src, return_value_policy policy, handle parent) { 314 return cast_impl(src, policy, parent); 315 } 316 // const pointer return 317 static handle cast(const Type *src, return_value_policy policy, handle parent) { 318 return cast_impl(src, policy, parent); 319 } 320 321 static PYBIND11_DESCR name() { return type_descr(props::descriptor()); } 322 323 operator Type*() { return &value; } 324 operator Type&() { return value; } 325 template <typename T> using cast_op_type = cast_op_type<T>; 326 327private: 328 Type value; 329}; 330 331// Eigen Ref/Map classes have slightly different policy requirements, meaning we don't want to force 332// `move` when a Ref/Map rvalue is returned; we treat Ref<> sort of like a pointer (we care about 333// the underlying data, not the outer shell). 334template <typename Return> 335struct return_value_policy_override<Return, enable_if_t<is_eigen_dense_map<Return>::value>> { 336 static return_value_policy policy(return_value_policy p) { return p; } 337}; 338 339// Base class for casting reference/map/block/etc. objects back to python. 340template <typename MapType> struct eigen_map_caster { 341private: 342 using props = EigenProps<MapType>; 343 344public: 345 346 // Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has 347 // to stay around), but we'll allow it under the assumption that you know what you're doing (and 348 // have an appropriate keep_alive in place). We return a numpy array pointing directly at the 349 // ref's data (The numpy array ends up read-only if the ref was to a const matrix type.) Note 350 // that this means you need to ensure you don't destroy the object in some other way (e.g. with 351 // an appropriate keep_alive, or with a reference to a statically allocated matrix). 352 static handle cast(const MapType &src, return_value_policy policy, handle parent) { 353 switch (policy) { 354 case return_value_policy::copy: 355 return eigen_array_cast<props>(src); 356 case return_value_policy::reference_internal: 357 return eigen_array_cast<props>(src, parent, is_eigen_mutable_map<MapType>::value); 358 case return_value_policy::reference: 359 case return_value_policy::automatic: 360 case return_value_policy::automatic_reference: 361 return eigen_array_cast<props>(src, none(), is_eigen_mutable_map<MapType>::value); 362 default: 363 // move, take_ownership don't make any sense for a ref/map: 364 pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type"); 365 } 366 } 367 368 static PYBIND11_DESCR name() { return props::descriptor(); } 369 370 // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return 371 // types but not bound arguments). We still provide them (with an explicitly delete) so that 372 // you end up here if you try anyway. 373 bool load(handle, bool) = delete; 374 operator MapType() = delete; 375 template <typename> using cast_op_type = MapType; 376}; 377 378// We can return any map-like object (but can only load Refs, specialized next): 379template <typename Type> struct type_caster<Type, enable_if_t<is_eigen_dense_map<Type>::value>> 380 : eigen_map_caster<Type> {}; 381 382// Loader for Ref<...> arguments. See the documentation for info on how to make this work without 383// copying (it requires some extra effort in many cases). 384template <typename PlainObjectType, typename StrideType> 385struct type_caster< 386 Eigen::Ref<PlainObjectType, 0, StrideType>, 387 enable_if_t<is_eigen_dense_map<Eigen::Ref<PlainObjectType, 0, StrideType>>::value> 388> : public eigen_map_caster<Eigen::Ref<PlainObjectType, 0, StrideType>> { 389private: 390 using Type = Eigen::Ref<PlainObjectType, 0, StrideType>; 391 using props = EigenProps<Type>; 392 using Scalar = typename props::Scalar; 393 using MapType = Eigen::Map<PlainObjectType, 0, StrideType>; 394 using Array = array_t<Scalar, array::forcecast | 395 ((props::row_major ? props::inner_stride : props::outer_stride) == 1 ? array::c_style : 396 (props::row_major ? props::outer_stride : props::inner_stride) == 1 ? array::f_style : 0)>; 397 static constexpr bool need_writeable = is_eigen_mutable_map<Type>::value; 398 // Delay construction (these have no default constructor) 399 std::unique_ptr<MapType> map; 400 std::unique_ptr<Type> ref; 401 // Our array. When possible, this is just a numpy array pointing to the source data, but 402 // sometimes we can't avoid copying (e.g. input is not a numpy array at all, has an incompatible 403 // layout, or is an array of a type that needs to be converted). Using a numpy temporary 404 // (rather than an Eigen temporary) saves an extra copy when we need both type conversion and 405 // storage order conversion. (Note that we refuse to use this temporary copy when loading an 406 // argument for a Ref<M> with M non-const, i.e. a read-write reference). 407 Array copy_or_ref; 408public: 409 bool load(handle src, bool convert) { 410 // First check whether what we have is already an array of the right type. If not, we can't 411 // avoid a copy (because the copy is also going to do type conversion). 412 bool need_copy = !isinstance<Array>(src); 413 414 EigenConformable<props::row_major> fits; 415 if (!need_copy) { 416 // We don't need a converting copy, but we also need to check whether the strides are 417 // compatible with the Ref's stride requirements 418 Array aref = reinterpret_borrow<Array>(src); 419 420 if (aref && (!need_writeable || aref.writeable())) { 421 fits = props::conformable(aref); 422 if (!fits) return false; // Incompatible dimensions 423 if (!fits.template stride_compatible<props>()) 424 need_copy = true; 425 else 426 copy_or_ref = std::move(aref); 427 } 428 else { 429 need_copy = true; 430 } 431 } 432 433 if (need_copy) { 434 // We need to copy: If we need a mutable reference, or we're not supposed to convert 435 // (either because we're in the no-convert overload pass, or because we're explicitly 436 // instructed not to copy (via `py::arg().noconvert()`) we have to fail loading. 437 if (!convert || need_writeable) return false; 438 439 Array copy = Array::ensure(src); 440 if (!copy) return false; 441 fits = props::conformable(copy); 442 if (!fits || !fits.template stride_compatible<props>()) 443 return false; 444 copy_or_ref = std::move(copy); 445 } 446 447 ref.reset(); 448 map.reset(new MapType(data(copy_or_ref), fits.rows, fits.cols, make_stride(fits.stride.outer(), fits.stride.inner()))); 449 ref.reset(new Type(*map)); 450 451 return true; 452 } 453 454 operator Type*() { return ref.get(); } 455 operator Type&() { return *ref; } 456 template <typename _T> using cast_op_type = pybind11::detail::cast_op_type<_T>; 457 458private: 459 template <typename T = Type, enable_if_t<is_eigen_mutable_map<T>::value, int> = 0> 460 Scalar *data(Array &a) { return a.mutable_data(); } 461 462 template <typename T = Type, enable_if_t<!is_eigen_mutable_map<T>::value, int> = 0> 463 const Scalar *data(Array &a) { return a.data(); } 464 465 // Attempt to figure out a constructor of `Stride` that will work. 466 // If both strides are fixed, use a default constructor: 467 template <typename S> using stride_ctor_default = bool_constant< 468 S::InnerStrideAtCompileTime != Eigen::Dynamic && S::OuterStrideAtCompileTime != Eigen::Dynamic && 469 std::is_default_constructible<S>::value>; 470 // Otherwise, if there is a two-index constructor, assume it is (outer,inner) like 471 // Eigen::Stride, and use it: 472 template <typename S> using stride_ctor_dual = bool_constant< 473 !stride_ctor_default<S>::value && std::is_constructible<S, EigenIndex, EigenIndex>::value>; 474 // Otherwise, if there is a one-index constructor, and just one of the strides is dynamic, use 475 // it (passing whichever stride is dynamic). 476 template <typename S> using stride_ctor_outer = bool_constant< 477 !any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value && 478 S::OuterStrideAtCompileTime == Eigen::Dynamic && S::InnerStrideAtCompileTime != Eigen::Dynamic && 479 std::is_constructible<S, EigenIndex>::value>; 480 template <typename S> using stride_ctor_inner = bool_constant< 481 !any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value && 482 S::InnerStrideAtCompileTime == Eigen::Dynamic && S::OuterStrideAtCompileTime != Eigen::Dynamic && 483 std::is_constructible<S, EigenIndex>::value>; 484 485 template <typename S = StrideType, enable_if_t<stride_ctor_default<S>::value, int> = 0> 486 static S make_stride(EigenIndex, EigenIndex) { return S(); } 487 template <typename S = StrideType, enable_if_t<stride_ctor_dual<S>::value, int> = 0> 488 static S make_stride(EigenIndex outer, EigenIndex inner) { return S(outer, inner); } 489 template <typename S = StrideType, enable_if_t<stride_ctor_outer<S>::value, int> = 0> 490 static S make_stride(EigenIndex outer, EigenIndex) { return S(outer); } 491 template <typename S = StrideType, enable_if_t<stride_ctor_inner<S>::value, int> = 0> 492 static S make_stride(EigenIndex, EigenIndex inner) { return S(inner); } 493 494}; 495 496// type_caster for special matrix types (e.g. DiagonalMatrix), which are EigenBase, but not 497// EigenDense (i.e. they don't have a data(), at least not with the usual matrix layout). 498// load() is not supported, but we can cast them into the python domain by first copying to a 499// regular Eigen::Matrix, then casting that. 500template <typename Type> 501struct type_caster<Type, enable_if_t<is_eigen_other<Type>::value>> { 502protected: 503 using Matrix = Eigen::Matrix<typename Type::Scalar, Type::RowsAtCompileTime, Type::ColsAtCompileTime>; 504 using props = EigenProps<Matrix>; 505public: 506 static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) { 507 handle h = eigen_encapsulate<props>(new Matrix(src)); 508 return h; 509 } 510 static handle cast(const Type *src, return_value_policy policy, handle parent) { return cast(*src, policy, parent); } 511 512 static PYBIND11_DESCR name() { return props::descriptor(); } 513 514 // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return 515 // types but not bound arguments). We still provide them (with an explicitly delete) so that 516 // you end up here if you try anyway. 517 bool load(handle, bool) = delete; 518 operator Type() = delete; 519 template <typename> using cast_op_type = Type; 520}; 521 522template<typename Type> 523struct type_caster<Type, enable_if_t<is_eigen_sparse<Type>::value>> { 524 typedef typename Type::Scalar Scalar; 525 typedef typename std::remove_reference<decltype(*std::declval<Type>().outerIndexPtr())>::type StorageIndex; 526 typedef typename Type::Index Index; 527 static constexpr bool rowMajor = Type::IsRowMajor; 528 529 bool load(handle src, bool) { 530 if (!src) 531 return false; 532 533 auto obj = reinterpret_borrow<object>(src); 534 object sparse_module = module::import("scipy.sparse"); 535 object matrix_type = sparse_module.attr( 536 rowMajor ? "csr_matrix" : "csc_matrix"); 537 538 if (obj.get_type() != matrix_type.ptr()) { 539 try { 540 obj = matrix_type(obj); 541 } catch (const error_already_set &) { 542 return false; 543 } 544 } 545 546 auto values = array_t<Scalar>((object) obj.attr("data")); 547 auto innerIndices = array_t<StorageIndex>((object) obj.attr("indices")); 548 auto outerIndices = array_t<StorageIndex>((object) obj.attr("indptr")); 549 auto shape = pybind11::tuple((pybind11::object) obj.attr("shape")); 550 auto nnz = obj.attr("nnz").cast<Index>(); 551 552 if (!values || !innerIndices || !outerIndices) 553 return false; 554 555 value = Eigen::MappedSparseMatrix<Scalar, Type::Flags, StorageIndex>( 556 shape[0].cast<Index>(), shape[1].cast<Index>(), nnz, 557 outerIndices.mutable_data(), innerIndices.mutable_data(), values.mutable_data()); 558 559 return true; 560 } 561 562 static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) { 563 const_cast<Type&>(src).makeCompressed(); 564 565 object matrix_type = module::import("scipy.sparse").attr( 566 rowMajor ? "csr_matrix" : "csc_matrix"); 567 568 array data((size_t) src.nonZeros(), src.valuePtr()); 569 array outerIndices((size_t) (rowMajor ? src.rows() : src.cols()) + 1, src.outerIndexPtr()); 570 array innerIndices((size_t) src.nonZeros(), src.innerIndexPtr()); 571 572 return matrix_type( 573 std::make_tuple(data, innerIndices, outerIndices), 574 std::make_pair(src.rows(), src.cols()) 575 ).release(); 576 } 577 578 PYBIND11_TYPE_CASTER(Type, _<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[", "scipy.sparse.csc_matrix[") 579 + npy_format_descriptor<Scalar>::name() + _("]")); 580}; 581 582NAMESPACE_END(detail) 583NAMESPACE_END(pybind11) 584 585#if defined(__GNUG__) || defined(__clang__) 586# pragma GCC diagnostic pop 587#elif defined(_MSC_VER) 588# pragma warning(pop) 589#endif 590