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pybind11/include/pybind11/numpy.h
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/*
pybind11/numpy.h: Basic NumPy support, vectorize() wrapper
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 "pybind11.h"
#include "complex.h"
#include <algorithm>
#include <array>
#include <cstdint>
#include <cstdlib>
#include <cstring>
#include <functional>
#include <numeric>
#include <sstream>
#include <string>
#include <type_traits>
#include <typeindex>
#include <utility>
#include <vector>
/* This will be true on all flat address space platforms and allows us to reduce the
whole npy_intp / ssize_t / Py_intptr_t business down to just ssize_t for all size
and dimension types (e.g. shape, strides, indexing), instead of inflicting this
upon the library user. */
static_assert(sizeof(::pybind11::ssize_t) == sizeof(Py_intptr_t), "ssize_t != Py_intptr_t");
static_assert(std::is_signed<Py_intptr_t>::value, "Py_intptr_t must be signed");
// We now can reinterpret_cast between py::ssize_t and Py_intptr_t (MSVC + PyPy cares)
PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
class array; // Forward declaration
template <typename>
struct numpy_scalar; // Forward declaration
PYBIND11_NAMESPACE_BEGIN(detail)
template <>
struct handle_type_name<array> {
static constexpr auto name = const_name("numpy.ndarray");
};
template <typename type, typename SFINAE = void>
struct npy_format_descriptor;
struct PyArrayDescr_Proxy {
PyObject_HEAD
PyObject *typeobj;
char kind;
char type;
char byteorder;
char flags;
int type_num;
int elsize;
int alignment;
char *subarray;
PyObject *fields;
PyObject *names;
};
struct PyArray_Proxy {
PyObject_HEAD
char *data;
int nd;
ssize_t *dimensions;
ssize_t *strides;
PyObject *base;
PyObject *descr;
int flags;
};
struct PyVoidScalarObject_Proxy {
PyObject_VAR_HEAD char *obval;
PyArrayDescr_Proxy *descr;
int flags;
PyObject *base;
};
struct numpy_type_info {
PyObject *dtype_ptr;
std::string format_str;
};
struct numpy_internals {
std::unordered_map<std::type_index, numpy_type_info> registered_dtypes;
numpy_type_info *get_type_info(const std::type_info &tinfo, bool throw_if_missing = true) {
auto it = registered_dtypes.find(std::type_index(tinfo));
if (it != registered_dtypes.end()) {
return &(it->second);
}
if (throw_if_missing) {
pybind11_fail(std::string("NumPy type info missing for ") + tinfo.name());
}
return nullptr;
}
template <typename T>
numpy_type_info *get_type_info(bool throw_if_missing = true) {
return get_type_info(typeid(typename std::remove_cv<T>::type), throw_if_missing);
}
};
PYBIND11_NOINLINE void load_numpy_internals(numpy_internals *&ptr) {
ptr = &get_or_create_shared_data<numpy_internals>("_numpy_internals");
}
inline numpy_internals &get_numpy_internals() {
static numpy_internals *ptr = nullptr;
if (!ptr) {
load_numpy_internals(ptr);
}
return *ptr;
}
template <typename T>
struct same_size {
template <typename U>
using as = bool_constant<sizeof(T) == sizeof(U)>;
};
template <std::size_t>
constexpr int platform_lookup() {
return -1;
}
// Lookup a type according to its size, and return a value corresponding to the NumPy typenum.
template <std::size_t size, typename T, typename... Ts, typename... Ints>
constexpr int platform_lookup(int I, Ints... Is) {
return sizeof(size) == sizeof(T) ? I : platform_lookup<size, Ts...>(Is...);
}
struct npy_api {
enum constants {
NPY_ARRAY_C_CONTIGUOUS_ = 0x0001,
NPY_ARRAY_F_CONTIGUOUS_ = 0x0002,
NPY_ARRAY_OWNDATA_ = 0x0004,
NPY_ARRAY_FORCECAST_ = 0x0010,
NPY_ARRAY_ENSUREARRAY_ = 0x0040,
NPY_ARRAY_ALIGNED_ = 0x0100,
NPY_ARRAY_WRITEABLE_ = 0x0400,
NPY_BOOL_ = 0,
NPY_BYTE_,
NPY_UBYTE_,
NPY_SHORT_,
NPY_USHORT_,
NPY_INT_,
NPY_UINT_,
NPY_LONG_,
NPY_ULONG_,
NPY_LONGLONG_,
NPY_ULONGLONG_,
NPY_FLOAT_,
NPY_DOUBLE_,
NPY_LONGDOUBLE_,
NPY_CFLOAT_,
NPY_CDOUBLE_,
NPY_CLONGDOUBLE_,
NPY_OBJECT_ = 17,
NPY_STRING_,
NPY_UNICODE_,
NPY_VOID_,
// Platform-dependent normalization
NPY_INT8_ = NPY_BYTE_,
NPY_UINT8_ = NPY_UBYTE_,
NPY_INT16_ = NPY_SHORT_,
NPY_UINT16_ = NPY_USHORT_,
// `npy_common.h` defines the integer aliases. In order, it checks:
// NPY_BITSOF_LONG, NPY_BITSOF_LONGLONG, NPY_BITSOF_INT, NPY_BITSOF_SHORT, NPY_BITSOF_CHAR
// and assigns the alias to the first matching size, so we should check in this order.
NPY_INT32_ = platform_lookup<4, long, int, short>(NPY_LONG_, NPY_INT_, NPY_SHORT_),
NPY_UINT32_ = platform_lookup<4, unsigned long, unsigned int, unsigned short>(
NPY_ULONG_, NPY_UINT_, NPY_USHORT_),
NPY_INT64_ = platform_lookup<8, long, long long, int>(NPY_LONG_, NPY_LONGLONG_, NPY_INT_),
NPY_UINT64_ = platform_lookup<8, unsigned long, unsigned long long, unsigned int>(
NPY_ULONG_, NPY_ULONGLONG_, NPY_UINT_),
NPY_FLOAT32_
= platform_lookup<4, double, float, long double>(NPY_DOUBLE_, NPY_FLOAT_, NPY_LONGDOUBLE_),
NPY_FLOAT64_
= platform_lookup<8, double, float, long double>(NPY_DOUBLE_, NPY_FLOAT_, NPY_LONGDOUBLE_),
NPY_COMPLEX64_
= platform_lookup<8, std::complex<double>, std::complex<float>, std::complex<long double>>(
NPY_DOUBLE_, NPY_FLOAT_, NPY_LONGDOUBLE_),
NPY_COMPLEX128_
= platform_lookup<8, std::complex<double>, std::complex<float>, std::complex<long double>>(
NPY_DOUBLE_, NPY_FLOAT_, NPY_LONGDOUBLE_),
NPY_CHAR_ = std::is_signed<char>::value ? NPY_BYTE_ : NPY_UBYTE_,
};
struct PyArray_Dims {
Py_intptr_t *ptr;
int len;
};
static npy_api &get() {
static npy_api api = lookup();
return api;
}
bool PyArray_Check_(PyObject *obj) const {
return PyObject_TypeCheck(obj, PyArray_Type_) != 0;
}
bool PyArrayDescr_Check_(PyObject *obj) const {
return PyObject_TypeCheck(obj, PyArrayDescr_Type_) != 0;
}
unsigned int (*PyArray_GetNDArrayCFeatureVersion_)();
PyObject *(*PyArray_DescrFromType_)(int);
PyObject *(*PyArray_TypeObjectFromType_)(int);
PyObject *(*PyArray_NewFromDescr_)(PyTypeObject *,
PyObject *,
int,
Py_intptr_t const *,
Py_intptr_t const *,
void *,
int,
PyObject *);
// Unused. Not removed because that affects ABI of the class.
PyObject *(*PyArray_DescrNewFromType_)(int);
int (*PyArray_CopyInto_)(PyObject *, PyObject *);
PyObject *(*PyArray_NewCopy_)(PyObject *, int);
PyTypeObject *PyArray_Type_;
PyTypeObject *PyVoidArrType_Type_;
PyTypeObject *PyArrayDescr_Type_;
PyObject *(*PyArray_DescrFromScalar_)(PyObject *);
PyObject *(*PyArray_Scalar_)(void *, PyObject *, PyObject *);
void (*PyArray_ScalarAsCtype_)(PyObject *, void *);
PyObject *(*PyArray_FromAny_)(PyObject *, PyObject *, int, int, int, PyObject *);
int (*PyArray_DescrConverter_)(PyObject *, PyObject **);
bool (*PyArray_EquivTypes_)(PyObject *, PyObject *);
int (*PyArray_GetArrayParamsFromObject_)(PyObject *,
PyObject *,
unsigned char,
PyObject **,
int *,
Py_intptr_t *,
PyObject **,
PyObject *);
PyObject *(*PyArray_Squeeze_)(PyObject *);
// Unused. Not removed because that affects ABI of the class.
int (*PyArray_SetBaseObject_)(PyObject *, PyObject *);
PyObject *(*PyArray_Resize_)(PyObject *, PyArray_Dims *, int, int);
PyObject *(*PyArray_Newshape_)(PyObject *, PyArray_Dims *, int);
PyObject *(*PyArray_View_)(PyObject *, PyObject *, PyObject *);
private:
enum functions {
API_PyArray_GetNDArrayCFeatureVersion = 211,
API_PyArray_Type = 2,
API_PyArrayDescr_Type = 3,
API_PyVoidArrType_Type = 39,
API_PyArray_DescrFromType = 45,
API_PyArray_TypeObjectFromType = 46,
API_PyArray_DescrFromScalar = 57,
API_PyArray_Scalar = 60,
API_PyArray_ScalarAsCtype = 62,
API_PyArray_FromAny = 69,
API_PyArray_Resize = 80,
API_PyArray_CopyInto = 82,
API_PyArray_NewCopy = 85,
API_PyArray_NewFromDescr = 94,
API_PyArray_DescrNewFromType = 96,
API_PyArray_Newshape = 135,
API_PyArray_Squeeze = 136,
API_PyArray_View = 137,
API_PyArray_DescrConverter = 174,
API_PyArray_EquivTypes = 182,
API_PyArray_GetArrayParamsFromObject = 278,
API_PyArray_SetBaseObject = 282
};
static npy_api lookup() {
module_ m = module_::import("numpy.core.multiarray");
auto c = m.attr("_ARRAY_API");
void **api_ptr = (void **) PyCapsule_GetPointer(c.ptr(), NULL);
npy_api api;
#define DECL_NPY_API(Func) api.Func##_ = (decltype(api.Func##_)) api_ptr[API_##Func];
DECL_NPY_API(PyArray_GetNDArrayCFeatureVersion);
if (api.PyArray_GetNDArrayCFeatureVersion_() < 0x7) {
pybind11_fail("pybind11 numpy support requires numpy >= 1.7.0");
}
DECL_NPY_API(PyArray_Type);
DECL_NPY_API(PyVoidArrType_Type);
DECL_NPY_API(PyArrayDescr_Type);
DECL_NPY_API(PyArray_DescrFromType);
DECL_NPY_API(PyArray_TypeObjectFromType);
DECL_NPY_API(PyArray_DescrFromScalar);
DECL_NPY_API(PyArray_Scalar);
DECL_NPY_API(PyArray_ScalarAsCtype);
DECL_NPY_API(PyArray_FromAny);
DECL_NPY_API(PyArray_Resize);
DECL_NPY_API(PyArray_CopyInto);
DECL_NPY_API(PyArray_NewCopy);
DECL_NPY_API(PyArray_NewFromDescr);
DECL_NPY_API(PyArray_DescrNewFromType);
DECL_NPY_API(PyArray_Newshape);
DECL_NPY_API(PyArray_Squeeze);
DECL_NPY_API(PyArray_View);
DECL_NPY_API(PyArray_DescrConverter);
DECL_NPY_API(PyArray_EquivTypes);
DECL_NPY_API(PyArray_GetArrayParamsFromObject);
DECL_NPY_API(PyArray_SetBaseObject);
#undef DECL_NPY_API
return api;
}
};
template <typename T>
struct is_complex : std::false_type {};
template <typename T>
struct is_complex<std::complex<T>> : std::true_type {};
template <typename T, typename = void>
struct npy_format_descriptor_name;
template <typename T>
struct npy_format_descriptor_name<T, enable_if_t<std::is_integral<T>::value>> {
static constexpr auto name = const_name<std::is_same<T, bool>::value>(
const_name("bool"),
const_name<std::is_signed<T>::value>("int", "uint") + const_name<sizeof(T) * 8>());
};
template <typename T>
struct npy_format_descriptor_name<T, enable_if_t<std::is_floating_point<T>::value>> {
static constexpr auto name
= const_name < std::is_same<T, float>::value
|| std::is_same<T, double>::value
> (const_name("float") + const_name<sizeof(T) * 8>(), const_name("longdouble"));
};
template <typename T>
struct npy_format_descriptor_name<T, enable_if_t<is_complex<T>::value>> {
static constexpr auto name
= const_name < std::is_same<typename T::value_type, float>::value
|| std::is_same<typename T::value_type, double>::value
> (const_name("complex") + const_name<sizeof(typename T::value_type) * 16>(),
const_name("longcomplex"));
};
template <typename T>
struct numpy_scalar_info {};
#define DECL_NPY_SCALAR(ctype_, typenum_) \
template <> \
struct numpy_scalar_info<ctype_> { \
static constexpr auto name = npy_format_descriptor_name<ctype_>::name; \
static constexpr int typenum = npy_api::typenum_##_; \
}
// boolean type
DECL_NPY_SCALAR(bool, NPY_BOOL);
// character types
DECL_NPY_SCALAR(char, NPY_CHAR);
DECL_NPY_SCALAR(signed char, NPY_BYTE);
DECL_NPY_SCALAR(unsigned char, NPY_UBYTE);
// signed integer types
DECL_NPY_SCALAR(std::int16_t, NPY_SHORT);
DECL_NPY_SCALAR(std::int32_t, NPY_INT);
DECL_NPY_SCALAR(std::int64_t, NPY_LONG);
#if defined(__linux__)
DECL_NPY_SCALAR(long long, NPY_LONG);
#else
DECL_NPY_SCALAR(long, NPY_LONG);
#endif
// unsigned integer types
DECL_NPY_SCALAR(std::uint16_t, NPY_USHORT);
DECL_NPY_SCALAR(std::uint32_t, NPY_UINT);
DECL_NPY_SCALAR(std::uint64_t, NPY_ULONG);
#if defined(__linux__)
DECL_NPY_SCALAR(unsigned long long, NPY_ULONG);
#else
DECL_NPY_SCALAR(unsigned long, NPY_ULONG);
#endif
// floating point types
DECL_NPY_SCALAR(float, NPY_FLOAT);
DECL_NPY_SCALAR(double, NPY_DOUBLE);
DECL_NPY_SCALAR(long double, NPY_LONGDOUBLE);
// complex types
DECL_NPY_SCALAR(std::complex<float>, NPY_CFLOAT);
DECL_NPY_SCALAR(std::complex<double>, NPY_CDOUBLE);
DECL_NPY_SCALAR(std::complex<long double>, NPY_CLONGDOUBLE);
#undef DECL_NPY_SCALAR
inline PyArray_Proxy *array_proxy(void *ptr) { return reinterpret_cast<PyArray_Proxy *>(ptr); }
inline const PyArray_Proxy *array_proxy(const void *ptr) {
return reinterpret_cast<const PyArray_Proxy *>(ptr);
}
inline PyArrayDescr_Proxy *array_descriptor_proxy(PyObject *ptr) {
return reinterpret_cast<PyArrayDescr_Proxy *>(ptr);
}
inline const PyArrayDescr_Proxy *array_descriptor_proxy(const PyObject *ptr) {
return reinterpret_cast<const PyArrayDescr_Proxy *>(ptr);
}
inline bool check_flags(const void *ptr, int flag) {
return (flag == (array_proxy(ptr)->flags & flag));
}
template <typename T>
struct is_std_array : std::false_type {};
template <typename T, size_t N>
struct is_std_array<std::array<T, N>> : std::true_type {};
template <typename T>
struct array_info_scalar {
using type = T;
static constexpr bool is_array = false;
static constexpr bool is_empty = false;
static constexpr auto extents = const_name("");
static void append_extents(list & /* shape */) {}
};
// Computes underlying type and a comma-separated list of extents for array
// types (any mix of std::array and built-in arrays). An array of char is
// treated as scalar because it gets special handling.
template <typename T>
struct array_info : array_info_scalar<T> {};
template <typename T, size_t N>
struct array_info<std::array<T, N>> {
using type = typename array_info<T>::type;
static constexpr bool is_array = true;
static constexpr bool is_empty = (N == 0) || array_info<T>::is_empty;
static constexpr size_t extent = N;
// appends the extents to shape
static void append_extents(list &shape) {
shape.append(N);
array_info<T>::append_extents(shape);
}
static constexpr auto extents = const_name<array_info<T>::is_array>(
concat(const_name<N>(), array_info<T>::extents), const_name<N>());
};
// For numpy we have special handling for arrays of characters, so we don't include
// the size in the array extents.
template <size_t N>
struct array_info<char[N]> : array_info_scalar<char[N]> {};
template <size_t N>
struct array_info<std::array<char, N>> : array_info_scalar<std::array<char, N>> {};
template <typename T, size_t N>
struct array_info<T[N]> : array_info<std::array<T, N>> {};
template <typename T>
using remove_all_extents_t = typename array_info<T>::type;
template <typename T>
using is_pod_struct
= all_of<std::is_standard_layout<T>, // since we're accessing directly in memory
// we need a standard layout type
#if defined(__GLIBCXX__) \
&& (__GLIBCXX__ < 20150422 || __GLIBCXX__ == 20150426 || __GLIBCXX__ == 20150623 \
|| __GLIBCXX__ == 20150626 || __GLIBCXX__ == 20160803)
// libstdc++ < 5 (including versions 4.8.5, 4.9.3 and 4.9.4 which were released after
// 5) don't implement is_trivially_copyable, so approximate it
std::is_trivially_destructible<T>,
satisfies_any_of<T, std::has_trivial_copy_constructor, std::has_trivial_copy_assign>,
#else
std::is_trivially_copyable<T>,
#endif
satisfies_none_of<T,
std::is_reference,
std::is_array,
is_std_array,
std::is_arithmetic,
is_complex,
std::is_enum>>;
// Replacement for std::is_pod (deprecated in C++20)
template <typename T>
using is_pod = all_of<std::is_standard_layout<T>, std::is_trivial<T>>;
template <ssize_t Dim = 0, typename Strides>
ssize_t byte_offset_unsafe(const Strides &) {
return 0;
}
template <ssize_t Dim = 0, typename Strides, typename... Ix>
ssize_t byte_offset_unsafe(const Strides &strides, ssize_t i, Ix... index) {
return i * strides[Dim] + byte_offset_unsafe<Dim + 1>(strides, index...);
}
/**
* Proxy class providing unsafe, unchecked const access to array data. This is constructed through
* the `unchecked<T, N>()` method of `array` or the `unchecked<N>()` method of `array_t<T>`. `Dims`
* will be -1 for dimensions determined at runtime.
*/
template <typename T, ssize_t Dims>
class unchecked_reference {
protected:
static constexpr bool Dynamic = Dims < 0;
const unsigned char *data_;
// Storing the shape & strides in local variables (i.e. these arrays) allows the compiler to
// make large performance gains on big, nested loops, but requires compile-time dimensions
conditional_t<Dynamic, const ssize_t *, std::array<ssize_t, (size_t) Dims>> shape_, strides_;
const ssize_t dims_;
friend class pybind11::array;
// Constructor for compile-time dimensions:
template <bool Dyn = Dynamic>
unchecked_reference(const void *data,
const ssize_t *shape,
const ssize_t *strides,
enable_if_t<!Dyn, ssize_t>)
: data_{reinterpret_cast<const unsigned char *>(data)}, dims_{Dims} {
for (size_t i = 0; i < (size_t) dims_; i++) {
shape_[i] = shape[i];
strides_[i] = strides[i];
}
}
// Constructor for runtime dimensions:
template <bool Dyn = Dynamic>
unchecked_reference(const void *data,
const ssize_t *shape,
const ssize_t *strides,
enable_if_t<Dyn, ssize_t> dims)
: data_{reinterpret_cast<const unsigned char *>(data)}, shape_{shape}, strides_{strides},
dims_{dims} {}
public:
/**
* Unchecked const reference access to data at the given indices. For a compile-time known
* number of dimensions, this requires the correct number of arguments; for run-time
* dimensionality, this is not checked (and so is up to the caller to use safely).
*/
template <typename... Ix>
const T &operator()(Ix... index) const {
static_assert(ssize_t{sizeof...(Ix)} == Dims || Dynamic,
"Invalid number of indices for unchecked array reference");
return *reinterpret_cast<const T *>(data_
+ byte_offset_unsafe(strides_, ssize_t(index)...));
}
/**
* Unchecked const reference access to data; this operator only participates if the reference
* is to a 1-dimensional array. When present, this is exactly equivalent to `obj(index)`.
*/
template <ssize_t D = Dims, typename = enable_if_t<D == 1 || Dynamic>>
const T &operator[](ssize_t index) const {
return operator()(index);
}
/// Pointer access to the data at the given indices.
template <typename... Ix>
const T *data(Ix... ix) const {
return &operator()(ssize_t(ix)...);
}
/// Returns the item size, i.e. sizeof(T)
constexpr static ssize_t itemsize() { return sizeof(T); }
/// Returns the shape (i.e. size) of dimension `dim`
ssize_t shape(ssize_t dim) const { return shape_[(size_t) dim]; }
/// Returns the number of dimensions of the array
ssize_t ndim() const { return dims_; }
/// Returns the total number of elements in the referenced array, i.e. the product of the
/// shapes
template <bool Dyn = Dynamic>
enable_if_t<!Dyn, ssize_t> size() const {
return std::accumulate(
shape_.begin(), shape_.end(), (ssize_t) 1, std::multiplies<ssize_t>());
}
template <bool Dyn = Dynamic>
enable_if_t<Dyn, ssize_t> size() const {
return std::accumulate(shape_, shape_ + ndim(), (ssize_t) 1, std::multiplies<ssize_t>());
}
/// Returns the total number of bytes used by the referenced data. Note that the actual span
/// in memory may be larger if the referenced array has non-contiguous strides (e.g. for a
/// slice).
ssize_t nbytes() const { return size() * itemsize(); }
};
template <typename T, ssize_t Dims>
class unchecked_mutable_reference : public unchecked_reference<T, Dims> {
friend class pybind11::array;
using ConstBase = unchecked_reference<T, Dims>;
using ConstBase::ConstBase;
using ConstBase::Dynamic;
public:
// Bring in const-qualified versions from base class
using ConstBase::operator();
using ConstBase::operator[];
/// Mutable, unchecked access to data at the given indices.
template <typename... Ix>
T &operator()(Ix... index) {
static_assert(ssize_t{sizeof...(Ix)} == Dims || Dynamic,
"Invalid number of indices for unchecked array reference");
return const_cast<T &>(ConstBase::operator()(index...));
}
/**
* Mutable, unchecked access data at the given index; this operator only participates if the
* reference is to a 1-dimensional array (or has runtime dimensions). When present, this is
* exactly equivalent to `obj(index)`.
*/
template <ssize_t D = Dims, typename = enable_if_t<D == 1 || Dynamic>>
T &operator[](ssize_t index) {
return operator()(index);
}
/// Mutable pointer access to the data at the given indices.
template <typename... Ix>
T *mutable_data(Ix... ix) {
return &operator()(ssize_t(ix)...);
}
};
template <typename T, ssize_t Dim>
struct type_caster<unchecked_reference<T, Dim>> {
static_assert(Dim == 0 && Dim > 0 /* always fail */,
"unchecked array proxy object is not castable");
};
template <typename T, ssize_t Dim>
struct type_caster<unchecked_mutable_reference<T, Dim>>
: type_caster<unchecked_reference<T, Dim>> {};
template <typename T>
struct type_caster<numpy_scalar<T>> {
using value_type = T;
using type_info = numpy_scalar_info<T>;
PYBIND11_TYPE_CASTER(numpy_scalar<T>, type_info::name);
static handle &target_type() {
static handle tp = npy_api::get().PyArray_TypeObjectFromType_(type_info::typenum);
return tp;
}
static handle &target_dtype() {
static handle tp = npy_api::get().PyArray_DescrFromType_(type_info::typenum);
return tp;
}
bool load(handle src, bool) {
if (isinstance(src, target_type())) {
npy_api::get().PyArray_ScalarAsCtype_(src.ptr(), &value.value);
return true;
}
return false;
}
static handle cast(numpy_scalar<T> src, return_value_policy, handle) {
return npy_api::get().PyArray_Scalar_(&src.value, target_dtype().ptr(), nullptr);
}
};
PYBIND11_NAMESPACE_END(detail)
template <typename T>
struct numpy_scalar {
using value_type = T;
value_type value;
numpy_scalar() = default;
numpy_scalar(value_type value) : value(value) {}
operator value_type() { return value; }
numpy_scalar &operator=(value_type value) {
this->value = value;
return *this;
}
};
template <typename T>
numpy_scalar<T> make_scalar(T value) {
return numpy_scalar<T>(value);
}
class dtype : public object {
public:
PYBIND11_OBJECT_DEFAULT(dtype, object, detail::npy_api::get().PyArrayDescr_Check_);
explicit dtype(const buffer_info &info) {
dtype descr(_dtype_from_pep3118()(PYBIND11_STR_TYPE(info.format)));
// If info.itemsize == 0, use the value calculated from the format string
m_ptr = descr.strip_padding(info.itemsize != 0 ? info.itemsize : descr.itemsize())
.release()
.ptr();
}
explicit dtype(const std::string &format) {
m_ptr = from_args(pybind11::str(format)).release().ptr();
}
explicit dtype(const char *format) : dtype(std::string(format)) {}
dtype(list names, list formats, list offsets, ssize_t itemsize) {
dict args;
args["names"] = std::move(names);
args["formats"] = std::move(formats);
args["offsets"] = std::move(offsets);
args["itemsize"] = pybind11::int_(itemsize);
m_ptr = from_args(std::move(args)).release().ptr();
}
/// This is essentially the same as calling numpy.dtype(args) in Python.
static dtype from_args(object args) {
PyObject *ptr = nullptr;
if ((detail::npy_api::get().PyArray_DescrConverter_(args.ptr(), &ptr) == 0) || !ptr) {
throw error_already_set();
}
return reinterpret_steal<dtype>(ptr);
}
/// Return dtype associated with a C++ type.
template <typename T>
static dtype of() {
return detail::npy_format_descriptor<typename std::remove_cv<T>::type>::dtype();
}
/// Size of the data type in bytes.
ssize_t itemsize() const { return detail::array_descriptor_proxy(m_ptr)->elsize; }
/// Returns true for structured data types.
bool has_fields() const { return detail::array_descriptor_proxy(m_ptr)->names != nullptr; }
/// Single-character code for dtype's kind.
/// For example, floating point types are 'f' and integral types are 'i'.
char kind() const { return detail::array_descriptor_proxy(m_ptr)->kind; }
/// Single-character for dtype's type.
/// For example, ``float`` is 'f', ``double`` 'd', ``int`` 'i', and ``long`` 'l'.
char char_() const {
// Note: The signature, `dtype::char_` follows the naming of NumPy's
// public Python API (i.e., ``dtype.char``), rather than its internal
// C API (``PyArray_Descr::type``).
return detail::array_descriptor_proxy(m_ptr)->type;
}
private:
static object _dtype_from_pep3118() {
static PyObject *obj = module_::import("numpy.core._internal")
.attr("_dtype_from_pep3118")
.cast<object>()
.release()
.ptr();
return reinterpret_borrow<object>(obj);
}
dtype strip_padding(ssize_t itemsize) {
// Recursively strip all void fields with empty names that are generated for
// padding fields (as of NumPy v1.11).
if (!has_fields()) {
return *this;
}
struct field_descr {
PYBIND11_STR_TYPE name;
object format;
pybind11::int_ offset;
};
std::vector<field_descr> field_descriptors;
for (auto field : attr("fields").attr("items")()) {
auto spec = field.cast<tuple>();
auto name = spec[0].cast<pybind11::str>();
auto format = spec[1].cast<tuple>()[0].cast<dtype>();
auto offset = spec[1].cast<tuple>()[1].cast<pybind11::int_>();
if ((len(name) == 0u) && format.kind() == 'V') {
continue;
}
field_descriptors.push_back(
{(PYBIND11_STR_TYPE) name, format.strip_padding(format.itemsize()), offset});
}
std::sort(field_descriptors.begin(),
field_descriptors.end(),
[](const field_descr &a, const field_descr &b) {
return a.offset.cast<int>() < b.offset.cast<int>();
});
list names, formats, offsets;
for (auto &descr : field_descriptors) {
names.append(descr.name);
formats.append(descr.format);
offsets.append(descr.offset);
}
return dtype(std::move(names), std::move(formats), std::move(offsets), itemsize);
}
};
class array : public buffer {
public:
PYBIND11_OBJECT_CVT(array, buffer, detail::npy_api::get().PyArray_Check_, raw_array)
enum {
c_style = detail::npy_api::NPY_ARRAY_C_CONTIGUOUS_,
f_style = detail::npy_api::NPY_ARRAY_F_CONTIGUOUS_,
forcecast = detail::npy_api::NPY_ARRAY_FORCECAST_
};
array() : array(0, static_cast<const double *>(nullptr)) {}
using ShapeContainer = detail::any_container<ssize_t>;
using StridesContainer = detail::any_container<ssize_t>;
// Constructs an array taking shape/strides from arbitrary container types
array(const pybind11::dtype &dt,
ShapeContainer shape,
StridesContainer strides,
const void *ptr = nullptr,
handle base = handle()) {
if (strides->empty()) {
*strides = detail::c_strides(*shape, dt.itemsize());
}
auto ndim = shape->size();
if (ndim != strides->size()) {
pybind11_fail("NumPy: shape ndim doesn't match strides ndim");
}
auto descr = dt;
int flags = 0;
if (base && ptr) {
if (isinstance<array>(base)) {
/* Copy flags from base (except ownership bit) */
flags = reinterpret_borrow<array>(base).flags()
& ~detail::npy_api::NPY_ARRAY_OWNDATA_;
} else {
/* Writable by default, easy to downgrade later on if needed */
flags = detail::npy_api::NPY_ARRAY_WRITEABLE_;
}
}
auto &api = detail::npy_api::get();
auto tmp = reinterpret_steal<object>(api.PyArray_NewFromDescr_(
api.PyArray_Type_,
descr.release().ptr(),
(int) ndim,
// Use reinterpret_cast for PyPy on Windows (remove if fixed, checked on 7.3.1)
reinterpret_cast<Py_intptr_t *>(shape->data()),
reinterpret_cast<Py_intptr_t *>(strides->data()),
const_cast<void *>(ptr),
flags,
nullptr));
if (!tmp) {
throw error_already_set();
}
if (ptr) {
if (base) {
api.PyArray_SetBaseObject_(tmp.ptr(), base.inc_ref().ptr());
} else {
tmp = reinterpret_steal<object>(
api.PyArray_NewCopy_(tmp.ptr(), -1 /* any order */));
}
}
m_ptr = tmp.release().ptr();
}
array(const pybind11::dtype &dt,
ShapeContainer shape,
const void *ptr = nullptr,
handle base = handle())
: array(dt, std::move(shape), {}, ptr, base) {}
template <typename T,
typename
= detail::enable_if_t<std::is_integral<T>::value && !std::is_same<bool, T>::value>>
array(const pybind11::dtype &dt, T count, const void *ptr = nullptr, handle base = handle())
: array(dt, {{count}}, ptr, base) {}
template <typename T>
array(ShapeContainer shape, StridesContainer strides, const T *ptr, handle base = handle())
: array(pybind11::dtype::of<T>(), std::move(shape), std::move(strides), ptr, base) {}
template <typename T>
array(ShapeContainer shape, const T *ptr, handle base = handle())
: array(std::move(shape), {}, ptr, base) {}
template <typename T>
explicit array(ssize_t count, const T *ptr, handle base = handle())
: array({count}, {}, ptr, base) {}
explicit array(const buffer_info &info, handle base = handle())
: array(pybind11::dtype(info), info.shape, info.strides, info.ptr, base) {}
/// Array descriptor (dtype)
pybind11::dtype dtype() const {
return reinterpret_borrow<pybind11::dtype>(detail::array_proxy(m_ptr)->descr);
}
/// Total number of elements
ssize_t size() const {
return std::accumulate(shape(), shape() + ndim(), (ssize_t) 1, std::multiplies<ssize_t>());
}
/// Byte size of a single element
ssize_t itemsize() const {
return detail::array_descriptor_proxy(detail::array_proxy(m_ptr)->descr)->elsize;
}
/// Total number of bytes
ssize_t nbytes() const { return size() * itemsize(); }
/// Number of dimensions
ssize_t ndim() const { return detail::array_proxy(m_ptr)->nd; }
/// Base object
object base() const { return reinterpret_borrow<object>(detail::array_proxy(m_ptr)->base); }
/// Dimensions of the array
const ssize_t *shape() const { return detail::array_proxy(m_ptr)->dimensions; }
/// Dimension along a given axis
ssize_t shape(ssize_t dim) const {
if (dim >= ndim()) {
fail_dim_check(dim, "invalid axis");
}
return shape()[dim];
}
/// Strides of the array
const ssize_t *strides() const { return detail::array_proxy(m_ptr)->strides; }
/// Stride along a given axis
ssize_t strides(ssize_t dim) const {
if (dim >= ndim()) {
fail_dim_check(dim, "invalid axis");
}
return strides()[dim];
}
/// Return the NumPy array flags
int flags() const { return detail::array_proxy(m_ptr)->flags; }
/// If set, the array is writeable (otherwise the buffer is read-only)
bool writeable() const {
return detail::check_flags(m_ptr, detail::npy_api::NPY_ARRAY_WRITEABLE_);
}
/// If set, the array owns the data (will be freed when the array is deleted)
bool owndata() const {
return detail::check_flags(m_ptr, detail::npy_api::NPY_ARRAY_OWNDATA_);
}
/// Pointer to the contained data. If index is not provided, points to the
/// beginning of the buffer. May throw if the index would lead to out of bounds access.
template <typename... Ix>
const void *data(Ix... index) const {
return static_cast<const void *>(detail::array_proxy(m_ptr)->data + offset_at(index...));
}
/// Mutable pointer to the contained data. If index is not provided, points to the
/// beginning of the buffer. May throw if the index would lead to out of bounds access.
/// May throw if the array is not writeable.
template <typename... Ix>
void *mutable_data(Ix... index) {
check_writeable();
return static_cast<void *>(detail::array_proxy(m_ptr)->data + offset_at(index...));
}
/// Byte offset from beginning of the array to a given index (full or partial).
/// May throw if the index would lead to out of bounds access.
template <typename... Ix>
ssize_t offset_at(Ix... index) const {
if ((ssize_t) sizeof...(index) > ndim()) {
fail_dim_check(sizeof...(index), "too many indices for an array");
}
return byte_offset(ssize_t(index)...);
}
ssize_t offset_at() const { return 0; }
/// Item count from beginning of the array to a given index (full or partial).
/// May throw if the index would lead to out of bounds access.
template <typename... Ix>
ssize_t index_at(Ix... index) const {
return offset_at(index...) / itemsize();
}
/**
* Returns a proxy object that provides access to the array's data without bounds or
* dimensionality checking. Will throw if the array is missing the `writeable` flag. Use with
* care: the array must not be destroyed or reshaped for the duration of the returned object,
* and the caller must take care not to access invalid dimensions or dimension indices.
*/
template <typename T, ssize_t Dims = -1>
detail::unchecked_mutable_reference<T, Dims> mutable_unchecked() & {
if (PYBIND11_SILENCE_MSVC_C4127(Dims >= 0) && ndim() != Dims) {
throw std::domain_error("array has incorrect number of dimensions: "
+ std::to_string(ndim()) + "; expected "
+ std::to_string(Dims));
}
return detail::unchecked_mutable_reference<T, Dims>(
mutable_data(), shape(), strides(), ndim());
}
/**
* Returns a proxy object that provides const access to the array's data without bounds or
* dimensionality checking. Unlike `mutable_unchecked()`, this does not require that the
* underlying array have the `writable` flag. Use with care: the array must not be destroyed
* or reshaped for the duration of the returned object, and the caller must take care not to
* access invalid dimensions or dimension indices.
*/
template <typename T, ssize_t Dims = -1>
detail::unchecked_reference<T, Dims> unchecked() const & {
if (PYBIND11_SILENCE_MSVC_C4127(Dims >= 0) && ndim() != Dims) {
throw std::domain_error("array has incorrect number of dimensions: "
+ std::to_string(ndim()) + "; expected "
+ std::to_string(Dims));
}
return detail::unchecked_reference<T, Dims>(data(), shape(), strides(), ndim());
}
/// Return a new view with all of the dimensions of length 1 removed
array squeeze() {
auto &api = detail::npy_api::get();
return reinterpret_steal<array>(api.PyArray_Squeeze_(m_ptr));
}
/// Resize array to given shape
/// If refcheck is true and more that one reference exist to this array
/// then resize will succeed only if it makes a reshape, i.e. original size doesn't change
void resize(ShapeContainer new_shape, bool refcheck = true) {
detail::npy_api::PyArray_Dims d
= {// Use reinterpret_cast for PyPy on Windows (remove if fixed, checked on 7.3.1)
reinterpret_cast<Py_intptr_t *>(new_shape->data()),
int(new_shape->size())};
// try to resize, set ordering param to -1 cause it's not used anyway
auto new_array = reinterpret_steal<object>(
detail::npy_api::get().PyArray_Resize_(m_ptr, &d, int(refcheck), -1));
if (!new_array) {
throw error_already_set();
}
if (isinstance<array>(new_array)) {
*this = std::move(new_array);
}
}
/// Optional `order` parameter omitted, to be added as needed.
array reshape(ShapeContainer new_shape) {
detail::npy_api::PyArray_Dims d
= {reinterpret_cast<Py_intptr_t *>(new_shape->data()), int(new_shape->size())};
auto new_array
= reinterpret_steal<array>(detail::npy_api::get().PyArray_Newshape_(m_ptr, &d, 0));
if (!new_array) {
throw error_already_set();
}
return new_array;
}
/// Create a view of an array in a different data type.
/// This function may fundamentally reinterpret the data in the array.
/// It is the responsibility of the caller to ensure that this is safe.
/// Only supports the `dtype` argument, the `type` argument is omitted,
/// to be added as needed.
array view(const std::string &dtype) {
auto &api = detail::npy_api::get();
auto new_view = reinterpret_steal<array>(api.PyArray_View_(
m_ptr, dtype::from_args(pybind11::str(dtype)).release().ptr(), nullptr));
if (!new_view) {
throw error_already_set();
}
return new_view;
}
/// Ensure that the argument is a NumPy array
/// In case of an error, nullptr is returned and the Python error is cleared.
static array ensure(handle h, int ExtraFlags = 0) {
auto result = reinterpret_steal<array>(raw_array(h.ptr(), ExtraFlags));
if (!result) {
PyErr_Clear();
}
return result;
}
protected:
template <typename, typename>
friend struct detail::npy_format_descriptor;
void fail_dim_check(ssize_t dim, const std::string &msg) const {
throw index_error(msg + ": " + std::to_string(dim) + " (ndim = " + std::to_string(ndim())
+ ")");
}
template <typename... Ix>
ssize_t byte_offset(Ix... index) const {
check_dimensions(index...);
return detail::byte_offset_unsafe(strides(), ssize_t(index)...);
}
void check_writeable() const {
if (!writeable()) {
throw std::domain_error("array is not writeable");
}
}
template <typename... Ix>
void check_dimensions(Ix... index) const {
check_dimensions_impl(ssize_t(0), shape(), ssize_t(index)...);
}
void check_dimensions_impl(ssize_t, const ssize_t *) const {}
template <typename... Ix>
void check_dimensions_impl(ssize_t axis, const ssize_t *shape, ssize_t i, Ix... index) const {
if (i >= *shape) {
throw index_error(std::string("index ") + std::to_string(i)
+ " is out of bounds for axis " + std::to_string(axis)
+ " with size " + std::to_string(*shape));
}
check_dimensions_impl(axis + 1, shape + 1, index...);
}
/// Create array from any object -- always returns a new reference
static PyObject *raw_array(PyObject *ptr, int ExtraFlags = 0) {
if (ptr == nullptr) {
PyErr_SetString(PyExc_ValueError, "cannot create a pybind11::array from a nullptr");
return nullptr;
}
return detail::npy_api::get().PyArray_FromAny_(
ptr, nullptr, 0, 0, detail::npy_api::NPY_ARRAY_ENSUREARRAY_ | ExtraFlags, nullptr);
}
};
template <typename T, int ExtraFlags = array::forcecast>
class array_t : public array {
private:
struct private_ctor {};
// Delegating constructor needed when both moving and accessing in the same constructor
array_t(private_ctor,
ShapeContainer &&shape,
StridesContainer &&strides,
const T *ptr,
handle base)
: array(std::move(shape), std::move(strides), ptr, base) {}
public:
static_assert(!detail::array_info<T>::is_array, "Array types cannot be used with array_t");
using value_type = T;
array_t() : array(0, static_cast<const T *>(nullptr)) {}
array_t(handle h, borrowed_t) : array(h, borrowed_t{}) {}
array_t(handle h, stolen_t) : array(h, stolen_t{}) {}
PYBIND11_DEPRECATED("Use array_t<T>::ensure() instead")
array_t(handle h, bool is_borrowed) : array(raw_array_t(h.ptr()), stolen_t{}) {
if (!m_ptr) {
PyErr_Clear();
}
if (!is_borrowed) {
Py_XDECREF(h.ptr());
}
}
// NOLINTNEXTLINE(google-explicit-constructor)
array_t(const object &o) : array(raw_array_t(o.ptr()), stolen_t{}) {
if (!m_ptr) {
throw error_already_set();
}
}
explicit array_t(const buffer_info &info, handle base = handle()) : array(info, base) {}
array_t(ShapeContainer shape,
StridesContainer strides,
const T *ptr = nullptr,
handle base = handle())
: array(std::move(shape), std::move(strides), ptr, base) {}
explicit array_t(ShapeContainer shape, const T *ptr = nullptr, handle base = handle())
: array_t(private_ctor{},
std::move(shape),
(ExtraFlags & f_style) != 0 ? detail::f_strides(*shape, itemsize())
: detail::c_strides(*shape, itemsize()),
ptr,
base) {}
explicit array_t(ssize_t count, const T *ptr = nullptr, handle base = handle())
: array({count}, {}, ptr, base) {}
constexpr ssize_t itemsize() const { return sizeof(T); }
template <typename... Ix>
ssize_t index_at(Ix... index) const {
return offset_at(index...) / itemsize();
}
template <typename... Ix>
const T *data(Ix... index) const {
return static_cast<const T *>(array::data(index...));
}
template <typename... Ix>
T *mutable_data(Ix... index) {
return static_cast<T *>(array::mutable_data(index...));
}
// Reference to element at a given index
template <typename... Ix>
const T &at(Ix... index) const {
if ((ssize_t) sizeof...(index) != ndim()) {
fail_dim_check(sizeof...(index), "index dimension mismatch");
}
return *(static_cast<const T *>(array::data())
+ byte_offset(ssize_t(index)...) / itemsize());
}
// Mutable reference to element at a given index
template <typename... Ix>
T &mutable_at(Ix... index) {
if ((ssize_t) sizeof...(index) != ndim()) {
fail_dim_check(sizeof...(index), "index dimension mismatch");
}
return *(static_cast<T *>(array::mutable_data())
+ byte_offset(ssize_t(index)...) / itemsize());
}
/**
* Returns a proxy object that provides access to the array's data without bounds or
* dimensionality checking. Will throw if the array is missing the `writeable` flag. Use with
* care: the array must not be destroyed or reshaped for the duration of the returned object,
* and the caller must take care not to access invalid dimensions or dimension indices.
*/
template <ssize_t Dims = -1>
detail::unchecked_mutable_reference<T, Dims> mutable_unchecked() & {
return array::mutable_unchecked<T, Dims>();
}
/**
* Returns a proxy object that provides const access to the array's data without bounds or
* dimensionality checking. Unlike `unchecked()`, this does not require that the underlying
* array have the `writable` flag. Use with care: the array must not be destroyed or reshaped
* for the duration of the returned object, and the caller must take care not to access invalid
* dimensions or dimension indices.
*/
template <ssize_t Dims = -1>
detail::unchecked_reference<T, Dims> unchecked() const & {
return array::unchecked<T, Dims>();
}
/// Ensure that the argument is a NumPy array of the correct dtype (and if not, try to convert
/// it). In case of an error, nullptr is returned and the Python error is cleared.
static array_t ensure(handle h) {
auto result = reinterpret_steal<array_t>(raw_array_t(h.ptr()));
if (!result) {
PyErr_Clear();
}
return result;
}
static bool check_(handle h) {
const auto &api = detail::npy_api::get();
return api.PyArray_Check_(h.ptr())
&& api.PyArray_EquivTypes_(detail::array_proxy(h.ptr())->descr,
dtype::of<T>().ptr())
&& detail::check_flags(h.ptr(), ExtraFlags & (array::c_style | array::f_style));
}
protected:
/// Create array from any object -- always returns a new reference
static PyObject *raw_array_t(PyObject *ptr) {
if (ptr == nullptr) {
PyErr_SetString(PyExc_ValueError, "cannot create a pybind11::array_t from a nullptr");
return nullptr;
}
return detail::npy_api::get().PyArray_FromAny_(ptr,
dtype::of<T>().release().ptr(),
0,
0,
detail::npy_api::NPY_ARRAY_ENSUREARRAY_
| ExtraFlags,
nullptr);
}
};
template <typename T>
struct format_descriptor<T, detail::enable_if_t<detail::is_pod_struct<T>::value>> {
static std::string format() {
return detail::npy_format_descriptor<typename std::remove_cv<T>::type>::format();
}
};
template <size_t N>
struct format_descriptor<char[N]> {
static std::string format() { return std::to_string(N) + "s"; }
};
template <size_t N>
struct format_descriptor<std::array<char, N>> {
static std::string format() { return std::to_string(N) + "s"; }
};
template <typename T>
struct format_descriptor<T, detail::enable_if_t<std::is_enum<T>::value>> {
static std::string format() {
return format_descriptor<
typename std::remove_cv<typename std::underlying_type<T>::type>::type>::format();
}
};
template <typename T>
struct format_descriptor<T, detail::enable_if_t<detail::array_info<T>::is_array>> {
static std::string format() {
using namespace detail;
static constexpr auto extents = const_name("(") + array_info<T>::extents + const_name(")");
return extents.text + format_descriptor<remove_all_extents_t<T>>::format();
}
};
PYBIND11_NAMESPACE_BEGIN(detail)
template <typename T, int ExtraFlags>
struct pyobject_caster<array_t<T, ExtraFlags>> {
using type = array_t<T, ExtraFlags>;
bool load(handle src, bool convert) {
if (!convert && !type::check_(src)) {
return false;
}
value = type::ensure(src);
return static_cast<bool>(value);
}
static handle cast(const handle &src, return_value_policy /* policy */, handle /* parent */) {
return src.inc_ref();
}
PYBIND11_TYPE_CASTER(type, handle_type_name<type>::name);
};
template <typename T>
struct compare_buffer_info<T, detail::enable_if_t<detail::is_pod_struct<T>::value>> {
static bool compare(const buffer_info &b) {
return npy_api::get().PyArray_EquivTypes_(dtype::of<T>().ptr(), dtype(b).ptr());
}
};
template <typename T>
struct npy_format_descriptor<
T,
enable_if_t<satisfies_any_of<T, std::is_arithmetic, is_complex>::value>>
: npy_format_descriptor_name<T> {
private:
// NB: the order here must match the one in common.h
constexpr static const int values[15] = {npy_api::NPY_BOOL_,
npy_api::NPY_BYTE_,
npy_api::NPY_UBYTE_,
npy_api::NPY_INT16_,
npy_api::NPY_UINT16_,
npy_api::NPY_INT32_,
npy_api::NPY_UINT32_,
npy_api::NPY_INT64_,
npy_api::NPY_UINT64_,
npy_api::NPY_FLOAT_,
npy_api::NPY_DOUBLE_,
npy_api::NPY_LONGDOUBLE_,
npy_api::NPY_CFLOAT_,
npy_api::NPY_CDOUBLE_,
npy_api::NPY_CLONGDOUBLE_};
public:
static constexpr int value = values[detail::is_fmt_numeric<T>::index];
static pybind11::dtype dtype() {
if (auto *ptr = npy_api::get().PyArray_DescrFromType_(value)) {
return reinterpret_steal<pybind11::dtype>(ptr);
}
pybind11_fail("Unsupported buffer format!");
}
};
#define PYBIND11_DECL_CHAR_FMT \
static constexpr auto name = const_name("S") + const_name<N>(); \
static pybind11::dtype dtype() { \
return pybind11::dtype(std::string("S") + std::to_string(N)); \
}
template <size_t N>
struct npy_format_descriptor<char[N]> {
PYBIND11_DECL_CHAR_FMT
};
template <size_t N>
struct npy_format_descriptor<std::array<char, N>> {
PYBIND11_DECL_CHAR_FMT
};
#undef PYBIND11_DECL_CHAR_FMT
template <typename T>
struct npy_format_descriptor<T, enable_if_t<array_info<T>::is_array>> {
private:
using base_descr = npy_format_descriptor<typename array_info<T>::type>;
public:
static_assert(!array_info<T>::is_empty, "Zero-sized arrays are not supported");
static constexpr auto name
= const_name("(") + array_info<T>::extents + const_name(")") + base_descr::name;
static pybind11::dtype dtype() {
list shape;
array_info<T>::append_extents(shape);
return pybind11::dtype::from_args(pybind11::make_tuple(base_descr::dtype(), shape));
}
};
template <typename T>
struct npy_format_descriptor<T, enable_if_t<std::is_enum<T>::value>> {
private:
using base_descr = npy_format_descriptor<typename std::underlying_type<T>::type>;
public:
static constexpr auto name = base_descr::name;
static pybind11::dtype dtype() { return base_descr::dtype(); }
};
struct field_descriptor {
const char *name;
ssize_t offset;
ssize_t size;
std::string format;
dtype descr;
};
PYBIND11_NOINLINE void register_structured_dtype(any_container<field_descriptor> fields,
const std::type_info &tinfo,
ssize_t itemsize,
bool (*direct_converter)(PyObject *, void *&)) {
auto &numpy_internals = get_numpy_internals();
if (numpy_internals.get_type_info(tinfo, false)) {
pybind11_fail("NumPy: dtype is already registered");
}
// Use ordered fields because order matters as of NumPy 1.14:
// https://docs.scipy.org/doc/numpy/release.html#multiple-field-indexing-assignment-of-structured-arrays
std::vector<field_descriptor> ordered_fields(std::move(fields));
std::sort(
ordered_fields.begin(),
ordered_fields.end(),
[](const field_descriptor &a, const field_descriptor &b) { return a.offset < b.offset; });
list names, formats, offsets;
for (auto &field : ordered_fields) {
if (!field.descr) {
pybind11_fail(std::string("NumPy: unsupported field dtype: `") + field.name + "` @ "
+ tinfo.name());
}
names.append(PYBIND11_STR_TYPE(field.name));
formats.append(field.descr);
offsets.append(pybind11::int_(field.offset));
}
auto *dtype_ptr
= pybind11::dtype(std::move(names), std::move(formats), std::move(offsets), itemsize)
.release()
.ptr();
// There is an existing bug in NumPy (as of v1.11): trailing bytes are
// not encoded explicitly into the format string. This will supposedly
// get fixed in v1.12; for further details, see these:
// - https://github.com/numpy/numpy/issues/7797
// - https://github.com/numpy/numpy/pull/7798
// Because of this, we won't use numpy's logic to generate buffer format
// strings and will just do it ourselves.
ssize_t offset = 0;
std::ostringstream oss;
// mark the structure as unaligned with '^', because numpy and C++ don't
// always agree about alignment (particularly for complex), and we're
// explicitly listing all our padding. This depends on none of the fields
// overriding the endianness. Putting the ^ in front of individual fields
// isn't guaranteed to work due to https://github.com/numpy/numpy/issues/9049
oss << "^T{";
for (auto &field : ordered_fields) {
if (field.offset > offset) {
oss << (field.offset - offset) << 'x';
}
oss << field.format << ':' << field.name << ':';
offset = field.offset + field.size;
}
if (itemsize > offset) {
oss << (itemsize - offset) << 'x';
}
oss << '}';
auto format_str = oss.str();
// Sanity check: verify that NumPy properly parses our buffer format string
auto &api = npy_api::get();
auto arr = array(buffer_info(nullptr, itemsize, format_str, 1));
if (!api.PyArray_EquivTypes_(dtype_ptr, arr.dtype().ptr())) {
pybind11_fail("NumPy: invalid buffer descriptor!");
}
auto tindex = std::type_index(tinfo);
numpy_internals.registered_dtypes[tindex] = {dtype_ptr, format_str};
get_internals().direct_conversions[tindex].push_back(direct_converter);
}
template <typename T, typename SFINAE>
struct npy_format_descriptor {
static_assert(is_pod_struct<T>::value,
"Attempt to use a non-POD or unimplemented POD type as a numpy dtype");
static constexpr auto name = make_caster<T>::name;
static pybind11::dtype dtype() { return reinterpret_borrow<pybind11::dtype>(dtype_ptr()); }
static std::string format() {
static auto format_str = get_numpy_internals().get_type_info<T>(true)->format_str;
return format_str;
}
static void register_dtype(any_container<field_descriptor> fields) {
register_structured_dtype(std::move(fields),
typeid(typename std::remove_cv<T>::type),
sizeof(T),
&direct_converter);
}
private:
static PyObject *dtype_ptr() {
static PyObject *ptr = get_numpy_internals().get_type_info<T>(true)->dtype_ptr;
return ptr;
}
static bool direct_converter(PyObject *obj, void *&value) {
auto &api = npy_api::get();
if (!PyObject_TypeCheck(obj, api.PyVoidArrType_Type_)) {
return false;
}
if (auto descr = reinterpret_steal<object>(api.PyArray_DescrFromScalar_(obj))) {
if (api.PyArray_EquivTypes_(dtype_ptr(), descr.ptr())) {
value = ((PyVoidScalarObject_Proxy *) obj)->obval;
return true;
}
}
return false;
}
};
#ifdef __CLION_IDE__ // replace heavy macro with dummy code for the IDE (doesn't affect code)
# define PYBIND11_NUMPY_DTYPE(Type, ...) ((void) 0)
# define PYBIND11_NUMPY_DTYPE_EX(Type, ...) ((void) 0)
#else
# define PYBIND11_FIELD_DESCRIPTOR_EX(T, Field, Name) \
::pybind11::detail::field_descriptor { \
Name, offsetof(T, Field), sizeof(decltype(std::declval<T>().Field)), \
::pybind11::format_descriptor<decltype(std::declval<T>().Field)>::format(), \
::pybind11::detail::npy_format_descriptor< \
decltype(std::declval<T>().Field)>::dtype() \
}
// Extract name, offset and format descriptor for a struct field
# define PYBIND11_FIELD_DESCRIPTOR(T, Field) PYBIND11_FIELD_DESCRIPTOR_EX(T, Field, # Field)
// The main idea of this macro is borrowed from https://github.com/swansontec/map-macro
// (C) William Swanson, Paul Fultz
# define PYBIND11_EVAL0(...) __VA_ARGS__
# define PYBIND11_EVAL1(...) PYBIND11_EVAL0(PYBIND11_EVAL0(PYBIND11_EVAL0(__VA_ARGS__)))
# define PYBIND11_EVAL2(...) PYBIND11_EVAL1(PYBIND11_EVAL1(PYBIND11_EVAL1(__VA_ARGS__)))
# define PYBIND11_EVAL3(...) PYBIND11_EVAL2(PYBIND11_EVAL2(PYBIND11_EVAL2(__VA_ARGS__)))
# define PYBIND11_EVAL4(...) PYBIND11_EVAL3(PYBIND11_EVAL3(PYBIND11_EVAL3(__VA_ARGS__)))
# define PYBIND11_EVAL(...) PYBIND11_EVAL4(PYBIND11_EVAL4(PYBIND11_EVAL4(__VA_ARGS__)))
# define PYBIND11_MAP_END(...)
# define PYBIND11_MAP_OUT
# define PYBIND11_MAP_COMMA ,
# define PYBIND11_MAP_GET_END() 0, PYBIND11_MAP_END
# define PYBIND11_MAP_NEXT0(test, next, ...) next PYBIND11_MAP_OUT
# define PYBIND11_MAP_NEXT1(test, next) PYBIND11_MAP_NEXT0(test, next, 0)
# define PYBIND11_MAP_NEXT(test, next) PYBIND11_MAP_NEXT1(PYBIND11_MAP_GET_END test, next)
# if defined(_MSC_VER) \
&& !defined(__clang__) // MSVC is not as eager to expand macros, hence this workaround
# define PYBIND11_MAP_LIST_NEXT1(test, next) \
PYBIND11_EVAL0(PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0))
# else
# define PYBIND11_MAP_LIST_NEXT1(test, next) \
PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0)
# endif
# define PYBIND11_MAP_LIST_NEXT(test, next) \
PYBIND11_MAP_LIST_NEXT1(PYBIND11_MAP_GET_END test, next)
# define PYBIND11_MAP_LIST0(f, t, x, peek, ...) \
f(t, x) PYBIND11_MAP_LIST_NEXT(peek, PYBIND11_MAP_LIST1)(f, t, peek, __VA_ARGS__)
# define PYBIND11_MAP_LIST1(f, t, x, peek, ...) \
f(t, x) PYBIND11_MAP_LIST_NEXT(peek, PYBIND11_MAP_LIST0)(f, t, peek, __VA_ARGS__)
// PYBIND11_MAP_LIST(f, t, a1, a2, ...) expands to f(t, a1), f(t, a2), ...
# define PYBIND11_MAP_LIST(f, t, ...) \
PYBIND11_EVAL(PYBIND11_MAP_LIST1(f, t, __VA_ARGS__, (), 0))
# define PYBIND11_NUMPY_DTYPE(Type, ...) \
::pybind11::detail::npy_format_descriptor<Type>::register_dtype( \
::std::vector<::pybind11::detail::field_descriptor>{ \
PYBIND11_MAP_LIST(PYBIND11_FIELD_DESCRIPTOR, Type, __VA_ARGS__)})
# if defined(_MSC_VER) && !defined(__clang__)
# define PYBIND11_MAP2_LIST_NEXT1(test, next) \
PYBIND11_EVAL0(PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0))
# else
# define PYBIND11_MAP2_LIST_NEXT1(test, next) \
PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0)
# endif
# define PYBIND11_MAP2_LIST_NEXT(test, next) \
PYBIND11_MAP2_LIST_NEXT1(PYBIND11_MAP_GET_END test, next)
# define PYBIND11_MAP2_LIST0(f, t, x1, x2, peek, ...) \
f(t, x1, x2) PYBIND11_MAP2_LIST_NEXT(peek, PYBIND11_MAP2_LIST1)(f, t, peek, __VA_ARGS__)
# define PYBIND11_MAP2_LIST1(f, t, x1, x2, peek, ...) \
f(t, x1, x2) PYBIND11_MAP2_LIST_NEXT(peek, PYBIND11_MAP2_LIST0)(f, t, peek, __VA_ARGS__)
// PYBIND11_MAP2_LIST(f, t, a1, a2, ...) expands to f(t, a1, a2), f(t, a3, a4), ...
# define PYBIND11_MAP2_LIST(f, t, ...) \
PYBIND11_EVAL(PYBIND11_MAP2_LIST1(f, t, __VA_ARGS__, (), 0))
# define PYBIND11_NUMPY_DTYPE_EX(Type, ...) \
::pybind11::detail::npy_format_descriptor<Type>::register_dtype( \
::std::vector<::pybind11::detail::field_descriptor>{ \
PYBIND11_MAP2_LIST(PYBIND11_FIELD_DESCRIPTOR_EX, Type, __VA_ARGS__)})
#endif // __CLION_IDE__
class common_iterator {
public:
using container_type = std::vector<ssize_t>;
using value_type = container_type::value_type;
using size_type = container_type::size_type;
common_iterator() : m_strides() {}
common_iterator(void *ptr, const container_type &strides, const container_type &shape)
: p_ptr(reinterpret_cast<char *>(ptr)), m_strides(strides.size()) {
m_strides.back() = static_cast<value_type>(strides.back());
for (size_type i = m_strides.size() - 1; i != 0; --i) {
size_type j = i - 1;
auto s = static_cast<value_type>(shape[i]);
m_strides[j] = strides[j] + m_strides[i] - strides[i] * s;
}
}
void increment(size_type dim) { p_ptr += m_strides[dim]; }
void *data() const { return p_ptr; }
private:
char *p_ptr{0};
container_type m_strides;
};
template <size_t N>
class multi_array_iterator {
public:
using container_type = std::vector<ssize_t>;
multi_array_iterator(const std::array<buffer_info, N> &buffers, const container_type &shape)
: m_shape(shape.size()), m_index(shape.size(), 0), m_common_iterator() {
// Manual copy to avoid conversion warning if using std::copy
for (size_t i = 0; i < shape.size(); ++i) {
m_shape[i] = shape[i];
}
container_type strides(shape.size());
for (size_t i = 0; i < N; ++i) {
init_common_iterator(buffers[i], shape, m_common_iterator[i], strides);
}
}
multi_array_iterator &operator++() {
for (size_t j = m_index.size(); j != 0; --j) {
size_t i = j - 1;
if (++m_index[i] != m_shape[i]) {
increment_common_iterator(i);
break;
}
m_index[i] = 0;
}
return *this;
}
template <size_t K, class T = void>
T *data() const {
return reinterpret_cast<T *>(m_common_iterator[K].data());
}
private:
using common_iter = common_iterator;
void init_common_iterator(const buffer_info &buffer,
const container_type &shape,
common_iter &iterator,
container_type &strides) {
auto buffer_shape_iter = buffer.shape.rbegin();
auto buffer_strides_iter = buffer.strides.rbegin();
auto shape_iter = shape.rbegin();
auto strides_iter = strides.rbegin();
while (buffer_shape_iter != buffer.shape.rend()) {
if (*shape_iter == *buffer_shape_iter) {
*strides_iter = *buffer_strides_iter;
} else {
*strides_iter = 0;
}
++buffer_shape_iter;
++buffer_strides_iter;
++shape_iter;
++strides_iter;
}
std::fill(strides_iter, strides.rend(), 0);
iterator = common_iter(buffer.ptr, strides, shape);
}
void increment_common_iterator(size_t dim) {
for (auto &iter : m_common_iterator) {
iter.increment(dim);
}
}
container_type m_shape;
container_type m_index;
std::array<common_iter, N> m_common_iterator;
};
enum class broadcast_trivial { non_trivial, c_trivial, f_trivial };
// Populates the shape and number of dimensions for the set of buffers. Returns a
// broadcast_trivial enum value indicating whether the broadcast is "trivial"--that is, has each
// buffer being either a singleton or a full-size, C-contiguous (`c_trivial`) or Fortran-contiguous
// (`f_trivial`) storage buffer; returns `non_trivial` otherwise.
template <size_t N>
broadcast_trivial
broadcast(const std::array<buffer_info, N> &buffers, ssize_t &ndim, std::vector<ssize_t> &shape) {
ndim = std::accumulate(
buffers.begin(), buffers.end(), ssize_t(0), [](ssize_t res, const buffer_info &buf) {
return std::max(res, buf.ndim);
});
shape.clear();
shape.resize((size_t) ndim, 1);
// Figure out the output size, and make sure all input arrays conform (i.e. are either size 1
// or the full size).
for (size_t i = 0; i < N; ++i) {
auto res_iter = shape.rbegin();
auto end = buffers[i].shape.rend();
for (auto shape_iter = buffers[i].shape.rbegin(); shape_iter != end;
++shape_iter, ++res_iter) {
const auto &dim_size_in = *shape_iter;
auto &dim_size_out = *res_iter;
// Each input dimension can either be 1 or `n`, but `n` values must match across
// buffers
if (dim_size_out == 1) {
dim_size_out = dim_size_in;
} else if (dim_size_in != 1 && dim_size_in != dim_size_out) {
pybind11_fail("pybind11::vectorize: incompatible size/dimension of inputs!");
}
}
}
bool trivial_broadcast_c = true;
bool trivial_broadcast_f = true;
for (size_t i = 0; i < N && (trivial_broadcast_c || trivial_broadcast_f); ++i) {
if (buffers[i].size == 1) {
continue;
}
// Require the same number of dimensions:
if (buffers[i].ndim != ndim) {
return broadcast_trivial::non_trivial;
}
// Require all dimensions be full-size:
if (!std::equal(buffers[i].shape.cbegin(), buffers[i].shape.cend(), shape.cbegin())) {
return broadcast_trivial::non_trivial;
}
// Check for C contiguity (but only if previous inputs were also C contiguous)
if (trivial_broadcast_c) {
ssize_t expect_stride = buffers[i].itemsize;
auto end = buffers[i].shape.crend();
for (auto shape_iter = buffers[i].shape.crbegin(),
stride_iter = buffers[i].strides.crbegin();
trivial_broadcast_c && shape_iter != end;
++shape_iter, ++stride_iter) {
if (expect_stride == *stride_iter) {
expect_stride *= *shape_iter;
} else {
trivial_broadcast_c = false;
}
}
}
// Check for Fortran contiguity (if previous inputs were also F contiguous)
if (trivial_broadcast_f) {
ssize_t expect_stride = buffers[i].itemsize;
auto end = buffers[i].shape.cend();
for (auto shape_iter = buffers[i].shape.cbegin(),
stride_iter = buffers[i].strides.cbegin();
trivial_broadcast_f && shape_iter != end;
++shape_iter, ++stride_iter) {
if (expect_stride == *stride_iter) {
expect_stride *= *shape_iter;
} else {
trivial_broadcast_f = false;
}
}
}
}
return trivial_broadcast_c ? broadcast_trivial::c_trivial
: trivial_broadcast_f ? broadcast_trivial::f_trivial
: broadcast_trivial::non_trivial;
}
template <typename T>
struct vectorize_arg {
static_assert(!std::is_rvalue_reference<T>::value,
"Functions with rvalue reference arguments cannot be vectorized");
// The wrapped function gets called with this type:
using call_type = remove_reference_t<T>;
// Is this a vectorized argument?
static constexpr bool vectorize
= satisfies_any_of<call_type, std::is_arithmetic, is_complex, is_pod>::value
&& satisfies_none_of<call_type,
std::is_pointer,
std::is_array,
is_std_array,
std::is_enum>::value
&& (!std::is_reference<T>::value
|| (std::is_lvalue_reference<T>::value && std::is_const<call_type>::value));
// Accept this type: an array for vectorized types, otherwise the type as-is:
using type = conditional_t<vectorize, array_t<remove_cv_t<call_type>, array::forcecast>, T>;
};
// py::vectorize when a return type is present
template <typename Func, typename Return, typename... Args>
struct vectorize_returned_array {
using Type = array_t<Return>;
static Type create(broadcast_trivial trivial, const std::vector<ssize_t> &shape) {
if (trivial == broadcast_trivial::f_trivial) {
return array_t<Return, array::f_style>(shape);
}
return array_t<Return>(shape);
}
static Return *mutable_data(Type &array) { return array.mutable_data(); }
static Return call(Func &f, Args &...args) { return f(args...); }
static void call(Return *out, size_t i, Func &f, Args &...args) { out[i] = f(args...); }
};
// py::vectorize when a return type is not present
template <typename Func, typename... Args>
struct vectorize_returned_array<Func, void, Args...> {
using Type = none;
static Type create(broadcast_trivial, const std::vector<ssize_t> &) { return none(); }
static void *mutable_data(Type &) { return nullptr; }
static detail::void_type call(Func &f, Args &...args) {
f(args...);
return {};
}
static void call(void *, size_t, Func &f, Args &...args) { f(args...); }
};
template <typename Func, typename Return, typename... Args>
struct vectorize_helper {
// NVCC for some reason breaks if NVectorized is private
#ifdef __CUDACC__
public:
#else
private:
#endif
static constexpr size_t N = sizeof...(Args);
static constexpr size_t NVectorized = constexpr_sum(vectorize_arg<Args>::vectorize...);
static_assert(
NVectorized >= 1,
"pybind11::vectorize(...) requires a function with at least one vectorizable argument");
public:
template <typename T,
// SFINAE to prevent shadowing the copy constructor.
typename = detail::enable_if_t<
!std::is_same<vectorize_helper, typename std::decay<T>::type>::value>>
explicit vectorize_helper(T &&f) : f(std::forward<T>(f)) {}
object operator()(typename vectorize_arg<Args>::type... args) {
return run(args...,
make_index_sequence<N>(),
select_indices<vectorize_arg<Args>::vectorize...>(),
make_index_sequence<NVectorized>());
}
private:
remove_reference_t<Func> f;
// Internal compiler error in MSVC 19.16.27025.1 (Visual Studio 2017 15.9.4), when compiling
// with "/permissive-" flag when arg_call_types is manually inlined.
using arg_call_types = std::tuple<typename vectorize_arg<Args>::call_type...>;
template <size_t Index>
using param_n_t = typename std::tuple_element<Index, arg_call_types>::type;
using returned_array = vectorize_returned_array<Func, Return, Args...>;
// Runs a vectorized function given arguments tuple and three index sequences:
// - Index is the full set of 0 ... (N-1) argument indices;
// - VIndex is the subset of argument indices with vectorized parameters, letting us access
// vectorized arguments (anything not in this sequence is passed through)
// - BIndex is a incremental sequence (beginning at 0) of the same size as VIndex, so that
// we can store vectorized buffer_infos in an array (argument VIndex has its buffer at
// index BIndex in the array).
template <size_t... Index, size_t... VIndex, size_t... BIndex>
object run(typename vectorize_arg<Args>::type &...args,
index_sequence<Index...> i_seq,
index_sequence<VIndex...> vi_seq,
index_sequence<BIndex...> bi_seq) {
// Pointers to values the function was called with; the vectorized ones set here will start
// out as array_t<T> pointers, but they will be changed them to T pointers before we make
// call the wrapped function. Non-vectorized pointers are left as-is.
std::array<void *, N> params{{&args...}};
// The array of `buffer_info`s of vectorized arguments:
std::array<buffer_info, NVectorized> buffers{
{reinterpret_cast<array *>(params[VIndex])->request()...}};
/* Determine dimensions parameters of output array */
ssize_t nd = 0;
std::vector<ssize_t> shape(0);
auto trivial = broadcast(buffers, nd, shape);
auto ndim = (size_t) nd;
size_t size
= std::accumulate(shape.begin(), shape.end(), (size_t) 1, std::multiplies<size_t>());
// If all arguments are 0-dimension arrays (i.e. single values) return a plain value (i.e.
// not wrapped in an array).
if (size == 1 && ndim == 0) {
PYBIND11_EXPAND_SIDE_EFFECTS(params[VIndex] = buffers[BIndex].ptr);
return cast(
returned_array::call(f, *reinterpret_cast<param_n_t<Index> *>(params[Index])...));
}
auto result = returned_array::create(trivial, shape);
if (size == 0) {
return std::move(result);
}
/* Call the function */
auto *mutable_data = returned_array::mutable_data(result);
if (trivial == broadcast_trivial::non_trivial) {
apply_broadcast(buffers, params, mutable_data, size, shape, i_seq, vi_seq, bi_seq);
} else {
apply_trivial(buffers, params, mutable_data, size, i_seq, vi_seq, bi_seq);
}
return std::move(result);
}
template <size_t... Index, size_t... VIndex, size_t... BIndex>
void apply_trivial(std::array<buffer_info, NVectorized> &buffers,
std::array<void *, N> &params,
Return *out,
size_t size,
index_sequence<Index...>,
index_sequence<VIndex...>,
index_sequence<BIndex...>) {
// Initialize an array of mutable byte references and sizes with references set to the
// appropriate pointer in `params`; as we iterate, we'll increment each pointer by its size
// (except for singletons, which get an increment of 0).
std::array<std::pair<unsigned char *&, const size_t>, NVectorized> vecparams{
{std::pair<unsigned char *&, const size_t>(
reinterpret_cast<unsigned char *&>(params[VIndex] = buffers[BIndex].ptr),
buffers[BIndex].size == 1 ? 0 : sizeof(param_n_t<VIndex>))...}};
for (size_t i = 0; i < size; ++i) {
returned_array::call(
out, i, f, *reinterpret_cast<param_n_t<Index> *>(params[Index])...);
for (auto &x : vecparams) {
x.first += x.second;
}
}
}
template <size_t... Index, size_t... VIndex, size_t... BIndex>
void apply_broadcast(std::array<buffer_info, NVectorized> &buffers,
std::array<void *, N> &params,
Return *out,
size_t size,
const std::vector<ssize_t> &output_shape,
index_sequence<Index...>,
index_sequence<VIndex...>,
index_sequence<BIndex...>) {
multi_array_iterator<NVectorized> input_iter(buffers, output_shape);
for (size_t i = 0; i < size; ++i, ++input_iter) {
PYBIND11_EXPAND_SIDE_EFFECTS((params[VIndex] = input_iter.template data<BIndex>()));
returned_array::call(
out, i, f, *reinterpret_cast<param_n_t<Index> *>(std::get<Index>(params))...);
}
}
};
template <typename Func, typename Return, typename... Args>
vectorize_helper<Func, Return, Args...> vectorize_extractor(const Func &f, Return (*)(Args...)) {
return detail::vectorize_helper<Func, Return, Args...>(f);
}
template <typename T, int Flags>
struct handle_type_name<array_t<T, Flags>> {
static constexpr auto name
= const_name("numpy.ndarray[") + npy_format_descriptor<T>::name + const_name("]");
};
PYBIND11_NAMESPACE_END(detail)
// Vanilla pointer vectorizer:
template <typename Return, typename... Args>
detail::vectorize_helper<Return (*)(Args...), Return, Args...> vectorize(Return (*f)(Args...)) {
return detail::vectorize_helper<Return (*)(Args...), Return, Args...>(f);
}
// lambda vectorizer:
template <typename Func, detail::enable_if_t<detail::is_lambda<Func>::value, int> = 0>
auto vectorize(Func &&f)
-> decltype(detail::vectorize_extractor(std::forward<Func>(f),
(detail::function_signature_t<Func> *) nullptr)) {
return detail::vectorize_extractor(std::forward<Func>(f),
(detail::function_signature_t<Func> *) nullptr);
}
// Vectorize a class method (non-const):
template <typename Return,
typename Class,
typename... Args,
typename Helper = detail::vectorize_helper<
decltype(std::mem_fn(std::declval<Return (Class::*)(Args...)>())),
Return,
Class *,
Args...>>
Helper vectorize(Return (Class::*f)(Args...)) {
return Helper(std::mem_fn(f));
}
// Vectorize a class method (const):
template <typename Return,
typename Class,
typename... Args,
typename Helper = detail::vectorize_helper<
decltype(std::mem_fn(std::declval<Return (Class::*)(Args...) const>())),
Return,
const Class *,
Args...>>
Helper vectorize(Return (Class::*f)(Args...) const) {
return Helper(std::mem_fn(f));
}
PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
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