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tempestpy/resources/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 <numeric>
#include <algorithm>
#include <array>
#include <cstdlib>
#include <cstring>
#include <sstream>
#include <string>
#include <initializer_list>
#include <functional>
#include <utility>
#include <typeindex>
#if defined(_MSC_VER)
# pragma warning(push)
# pragma warning(disable: 4127) // warning C4127: Conditional expression is constant
#endif
/* This will be true on all flat address space platforms and allows us to reduce the
whole npy_intp / size_t / Py_intptr_t business down to just size_t for all size
and dimension types (e.g. shape, strides, indexing), instead of inflicting this
upon the library user. */
static_assert(sizeof(size_t) == sizeof(Py_intptr_t), "size_t != Py_intptr_t");
NAMESPACE_BEGIN(pybind11)
class array; // Forward declaration
NAMESPACE_BEGIN(detail)
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);
}
};
inline 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;
}
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_
};
static npy_api& get() {
static npy_api api = lookup();
return api;
}
bool PyArray_Check_(PyObject *obj) const {
return (bool) PyObject_TypeCheck(obj, PyArray_Type_);
}
bool PyArrayDescr_Check_(PyObject *obj) const {
return (bool) PyObject_TypeCheck(obj, PyArrayDescr_Type_);
}
PyObject *(*PyArray_DescrFromType_)(int);
PyObject *(*PyArray_NewFromDescr_)
(PyTypeObject *, PyObject *, int, Py_intptr_t *,
Py_intptr_t *, void *, int, PyObject *);
PyObject *(*PyArray_DescrNewFromType_)(int);
PyObject *(*PyArray_NewCopy_)(PyObject *, int);
PyTypeObject *PyArray_Type_;
PyTypeObject *PyVoidArrType_Type_;
PyTypeObject *PyArrayDescr_Type_;
PyObject *(*PyArray_DescrFromScalar_)(PyObject *);
PyObject *(*PyArray_FromAny_) (PyObject *, PyObject *, int, int, int, PyObject *);
int (*PyArray_DescrConverter_) (PyObject *, PyObject **);
bool (*PyArray_EquivTypes_) (PyObject *, PyObject *);
int (*PyArray_GetArrayParamsFromObject_)(PyObject *, PyObject *, char, PyObject **, int *,
Py_ssize_t *, PyObject **, PyObject *);
PyObject *(*PyArray_Squeeze_)(PyObject *);
int (*PyArray_SetBaseObject_)(PyObject *, PyObject *);
private:
enum functions {
API_PyArray_Type = 2,
API_PyArrayDescr_Type = 3,
API_PyVoidArrType_Type = 39,
API_PyArray_DescrFromType = 45,
API_PyArray_DescrFromScalar = 57,
API_PyArray_FromAny = 69,
API_PyArray_NewCopy = 85,
API_PyArray_NewFromDescr = 94,
API_PyArray_DescrNewFromType = 9,
API_PyArray_DescrConverter = 174,
API_PyArray_EquivTypes = 182,
API_PyArray_GetArrayParamsFromObject = 278,
API_PyArray_Squeeze = 136,
API_PyArray_SetBaseObject = 282
};
static npy_api lookup() {
module m = module::import("numpy.core.multiarray");
auto c = m.attr("_ARRAY_API");
#if PY_MAJOR_VERSION >= 3
void **api_ptr = (void **) PyCapsule_GetPointer(c.ptr(), NULL);
#else
void **api_ptr = (void **) PyCObject_AsVoidPtr(c.ptr());
#endif
npy_api api;
#define DECL_NPY_API(Func) api.Func##_ = (decltype(api.Func##_)) api_ptr[API_##Func];
DECL_NPY_API(PyArray_Type);
DECL_NPY_API(PyVoidArrType_Type);
DECL_NPY_API(PyArrayDescr_Type);
DECL_NPY_API(PyArray_DescrFromType);
DECL_NPY_API(PyArray_DescrFromScalar);
DECL_NPY_API(PyArray_FromAny);
DECL_NPY_API(PyArray_NewCopy);
DECL_NPY_API(PyArray_NewFromDescr);
DECL_NPY_API(PyArray_DescrNewFromType);
DECL_NPY_API(PyArray_DescrConverter);
DECL_NPY_API(PyArray_EquivTypes);
DECL_NPY_API(PyArray_GetArrayParamsFromObject);
DECL_NPY_API(PyArray_Squeeze);
DECL_NPY_API(PyArray_SetBaseObject);
#undef DECL_NPY_API
return api;
}
};
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 is_complex : std::false_type { };
template <typename T> struct is_complex<std::complex<T>> : std::true_type { };
template <typename T> using is_pod_struct = all_of<
std::is_pod<T>, // since we're accessing directly in memory we need a POD type
satisfies_none_of<T, std::is_reference, std::is_array, is_std_array, std::is_arithmetic, is_complex, std::is_enum>
>;
template <size_t Dim = 0, typename Strides> size_t byte_offset_unsafe(const Strides &) { return 0; }
template <size_t Dim = 0, typename Strides, typename... Ix>
size_t byte_offset_unsafe(const Strides &strides, size_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 size_t *, std::array<size_t, (size_t) Dims>>
shape_, strides_;
const size_t dims_;
friend class pybind11::array;
// Constructor for compile-time dimensions:
template <bool Dyn = Dynamic>
unchecked_reference(const void *data, const size_t *shape, const size_t *strides, enable_if_t<!Dyn, size_t>)
: data_{reinterpret_cast<const unsigned char *>(data)}, dims_{Dims} {
for (size_t i = 0; i < dims_; i++) {
shape_[i] = shape[i];
strides_[i] = strides[i];
}
}
// Constructor for runtime dimensions:
template <bool Dyn = Dynamic>
unchecked_reference(const void *data, const size_t *shape, const size_t *strides, enable_if_t<Dyn, size_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(sizeof...(Ix) == Dims || Dynamic,
"Invalid number of indices for unchecked array reference");
return *reinterpret_cast<const T *>(data_ + byte_offset_unsafe(strides_, size_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 <size_t D = Dims, typename = enable_if_t<D == 1 || Dynamic>>
const T &operator[](size_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()(size_t(ix)...); }
/// Returns the item size, i.e. sizeof(T)
constexpr static size_t itemsize() { return sizeof(T); }
/// Returns the shape (i.e. size) of dimension `dim`
size_t shape(size_t dim) const { return shape_[dim]; }
/// Returns the number of dimensions of the array
size_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, size_t> size() const {
return std::accumulate(shape_.begin(), shape_.end(), (size_t) 1, std::multiplies<size_t>());
}
template <bool Dyn = Dynamic>
enable_if_t<Dyn, size_t> size() const {
return std::accumulate(shape_, shape_ + ndim(), (size_t) 1, std::multiplies<size_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).
size_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:
/// Mutable, unchecked access to data at the given indices.
template <typename... Ix> T& operator()(Ix... index) {
static_assert(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 <size_t D = Dims, typename = enable_if_t<D == 1 || Dynamic>>
T &operator[](size_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()(size_t(ix)...); }
};
template <typename T, size_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, size_t Dim>
struct type_caster<unchecked_mutable_reference<T, Dim>> : type_caster<unchecked_reference<T, Dim>> {};
NAMESPACE_END(detail)
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 ? info.itemsize : descr.itemsize()).release().ptr();
}
explicit dtype(const std::string &format) {
m_ptr = from_args(pybind11::str(format)).release().ptr();
}
dtype(const char *format) : dtype(std::string(format)) { }
dtype(list names, list formats, list offsets, size_t itemsize) {
dict args;
args["names"] = names;
args["formats"] = formats;
args["offsets"] = offsets;
args["itemsize"] = pybind11::int_(itemsize);
m_ptr = from_args(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.release().ptr(), &ptr) || !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.
size_t itemsize() const {
return (size_t) 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 type code.
char kind() const {
return detail::array_descriptor_proxy(m_ptr)->kind;
}
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(size_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) && 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(names, formats, 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)) {}
array(const pybind11::dtype &dt, const std::vector<size_t> &shape,
const std::vector<size_t> &strides, const void *ptr = nullptr,
handle base = handle()) {
auto& api = detail::npy_api::get();
auto ndim = shape.size();
if (shape.size() != 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 tmp = reinterpret_steal<object>(api.PyArray_NewFromDescr_(
api.PyArray_Type_, descr.release().ptr(), (int) ndim,
reinterpret_cast<Py_intptr_t *>(const_cast<size_t*>(shape.data())),
reinterpret_cast<Py_intptr_t *>(const_cast<size_t*>(strides.data())),
const_cast<void *>(ptr), flags, nullptr));
if (!tmp)
pybind11_fail("NumPy: unable to create array!");
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, const std::vector<size_t> &shape,
const void *ptr = nullptr, handle base = handle())
: array(dt, shape, default_strides(shape, dt.itemsize()), ptr, base) { }
array(const pybind11::dtype &dt, size_t count, const void *ptr = nullptr,
handle base = handle())
: array(dt, std::vector<size_t>{ count }, ptr, base) { }
template<typename T> array(const std::vector<size_t>& shape,
const std::vector<size_t>& strides,
const T* ptr, handle base = handle())
: array(pybind11::dtype::of<T>(), shape, strides, (const void *) ptr, base) { }
template <typename T>
array(const std::vector<size_t> &shape, const T *ptr,
handle base = handle())
: array(shape, default_strides(shape, sizeof(T)), ptr, base) { }
template <typename T>
array(size_t count, const T *ptr, handle base = handle())
: array(std::vector<size_t>{ count }, ptr, base) { }
explicit array(const buffer_info &info)
: array(pybind11::dtype(info), info.shape, info.strides, info.ptr) { }
/// Array descriptor (dtype)
pybind11::dtype dtype() const {
return reinterpret_borrow<pybind11::dtype>(detail::array_proxy(m_ptr)->descr);
}
/// Total number of elements
size_t size() const {
return std::accumulate(shape(), shape() + ndim(), (size_t) 1, std::multiplies<size_t>());
}
/// Byte size of a single element
size_t itemsize() const {
return (size_t) detail::array_descriptor_proxy(detail::array_proxy(m_ptr)->descr)->elsize;
}
/// Total number of bytes
size_t nbytes() const {
return size() * itemsize();
}
/// Number of dimensions
size_t ndim() const {
return (size_t) 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 size_t* shape() const {
return reinterpret_cast<const size_t *>(detail::array_proxy(m_ptr)->dimensions);
}
/// Dimension along a given axis
size_t shape(size_t dim) const {
if (dim >= ndim())
fail_dim_check(dim, "invalid axis");
return shape()[dim];
}
/// Strides of the array
const size_t* strides() const {
return reinterpret_cast<const size_t *>(detail::array_proxy(m_ptr)->strides);
}
/// Stride along a given axis
size_t strides(size_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> size_t offset_at(Ix... index) const {
if (sizeof...(index) > ndim())
fail_dim_check(sizeof...(index), "too many indices for an array");
return byte_offset(size_t(index)...);
}
size_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> size_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 (Dims >= 0 && ndim() != (size_t) 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 (Dims >= 0 && ndim() != (size_t) 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));
}
/// 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(size_t dim, const std::string& msg) const {
throw index_error(msg + ": " + std::to_string(dim) +
" (ndim = " + std::to_string(ndim()) + ")");
}
template<typename... Ix> size_t byte_offset(Ix... index) const {
check_dimensions(index...);
return detail::byte_offset_unsafe(strides(), size_t(index)...);
}
void check_writeable() const {
if (!writeable())
throw std::domain_error("array is not writeable");
}
static std::vector<size_t> default_strides(const std::vector<size_t>& shape, size_t itemsize) {
auto ndim = shape.size();
std::vector<size_t> strides(ndim);
if (ndim) {
std::fill(strides.begin(), strides.end(), itemsize);
for (size_t i = 0; i < ndim - 1; i++)
for (size_t j = 0; j < ndim - 1 - i; j++)
strides[j] *= shape[ndim - 1 - i];
}
return strides;
}
template<typename... Ix> void check_dimensions(Ix... index) const {
check_dimensions_impl(size_t(0), shape(), size_t(index)...);
}
void check_dimensions_impl(size_t, const size_t*) const { }
template<typename... Ix> void check_dimensions_impl(size_t axis, const size_t* shape, size_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)
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 {
public:
using value_type = T;
array_t() : array(0, static_cast<const T *>(nullptr)) {}
array_t(handle h, borrowed_t) : array(h, borrowed) { }
array_t(handle h, stolen_t) : array(h, stolen) { }
PYBIND11_DEPRECATED("Use array_t<T>::ensure() instead")
array_t(handle h, bool is_borrowed) : array(raw_array_t(h.ptr()), stolen) {
if (!m_ptr) PyErr_Clear();
if (!is_borrowed) Py_XDECREF(h.ptr());
}
array_t(const object &o) : array(raw_array_t(o.ptr()), stolen) {
if (!m_ptr) throw error_already_set();
}
explicit array_t(const buffer_info& info) : array(info) { }
array_t(const std::vector<size_t> &shape,
const std::vector<size_t> &strides, const T *ptr = nullptr,
handle base = handle())
: array(shape, strides, ptr, base) { }
explicit array_t(const std::vector<size_t> &shape, const T *ptr = nullptr,
handle base = handle())
: array(shape, ptr, base) { }
explicit array_t(size_t count, const T *ptr = nullptr, handle base = handle())
: array(count, ptr, base) { }
constexpr size_t itemsize() const {
return sizeof(T);
}
template<typename... Ix> size_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 (sizeof...(index) != ndim())
fail_dim_check(sizeof...(index), "index dimension mismatch");
return *(static_cast<const T*>(array::data()) + byte_offset(size_t(index)...) / itemsize());
}
// Mutable reference to element at a given index
template<typename... Ix> T& mutable_at(Ix... index) {
if (sizeof...(index) != ndim())
fail_dim_check(sizeof...(index), "index dimension mismatch");
return *(static_cast<T*>(array::mutable_data()) + byte_offset(size_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());
}
protected:
/// Create array from any object -- always returns a new reference
static PyObject *raw_array_t(PyObject *ptr) {
if (ptr == 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();
}
};
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>> {
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_SHORT_, npy_api::NPY_USHORT_,
npy_api::NPY_INT_, npy_api::NPY_UINT_, npy_api::NPY_LONGLONG_, npy_api::NPY_ULONGLONG_,
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_borrow<pybind11::dtype>(ptr);
pybind11_fail("Unsupported buffer format!");
}
template <typename T2 = T, enable_if_t<std::is_integral<T2>::value, int> = 0>
static PYBIND11_DESCR name() {
return _<std::is_same<T, bool>::value>(_("bool"),
_<std::is_signed<T>::value>("int", "uint") + _<sizeof(T)*8>());
}
template <typename T2 = T, enable_if_t<std::is_floating_point<T2>::value, int> = 0>
static PYBIND11_DESCR name() {
return _<std::is_same<T, float>::value || std::is_same<T, double>::value>(
_("float") + _<sizeof(T)*8>(), _("longdouble"));
}
template <typename T2 = T, enable_if_t<is_complex<T2>::value, int> = 0>
static PYBIND11_DESCR name() {
return _<std::is_same<typename T2::value_type, float>::value || std::is_same<typename T2::value_type, double>::value>(
_("complex") + _<sizeof(typename T2::value_type)*16>(), _("longcomplex"));
}
};
#define PYBIND11_DECL_CHAR_FMT \
static PYBIND11_DESCR name() { return _("S") + _<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<std::is_enum<T>::value>> {
private:
using base_descr = npy_format_descriptor<typename std::underlying_type<T>::type>;
public:
static PYBIND11_DESCR name() { return base_descr::name(); }
static pybind11::dtype dtype() { return base_descr::dtype(); }
};
struct field_descriptor {
const char *name;
size_t offset;
size_t size;
size_t alignment;
std::string format;
dtype descr;
};
inline PYBIND11_NOINLINE void register_structured_dtype(
const std::initializer_list<field_descriptor>& fields,
const std::type_info& tinfo, size_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");
list names, formats, offsets;
for (auto field : 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(names, formats, 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.
std::vector<field_descriptor> ordered_fields(fields);
std::sort(ordered_fields.begin(), ordered_fields.end(),
[](const field_descriptor &a, const field_descriptor &b) { return a.offset < b.offset; });
size_t offset = 0;
std::ostringstream oss;
oss << "T{";
for (auto& field : ordered_fields) {
if (field.offset > offset)
oss << (field.offset - offset) << 'x';
// mark unaligned fields with '^' (unaligned native type)
if (field.offset % field.alignment)
oss << '^';
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 PYBIND11_DESCR name() { return 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(const std::initializer_list<field_descriptor>& fields) {
register_structured_dtype(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;
}
};
#define PYBIND11_FIELD_DESCRIPTOR_EX(T, Field, Name) \
::pybind11::detail::field_descriptor { \
Name, offsetof(T, Field), sizeof(decltype(std::declval<T>().Field)), \
alignof(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)
#ifdef _MSC_VER // 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 \
({PYBIND11_MAP_LIST (PYBIND11_FIELD_DESCRIPTOR, Type, __VA_ARGS__)})
#ifdef _MSC_VER
#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 \
({PYBIND11_MAP2_LIST (PYBIND11_FIELD_DESCRIPTOR_EX, Type, __VA_ARGS__)})
template <class T>
using array_iterator = typename std::add_pointer<T>::type;
template <class T>
array_iterator<T> array_begin(const buffer_info& buffer) {
return array_iterator<T>(reinterpret_cast<T*>(buffer.ptr));
}
template <class T>
array_iterator<T> array_end(const buffer_info& buffer) {
return array_iterator<T>(reinterpret_cast<T*>(buffer.ptr) + buffer.size);
}
class common_iterator {
public:
using container_type = std::vector<size_t>;
using value_type = container_type::value_type;
using size_type = container_type::size_type;
common_iterator() : p_ptr(0), m_strides() {}
common_iterator(void* ptr, const container_type& strides, const std::vector<size_t>& 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;
value_type 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;
container_type m_strides;
};
template <size_t N> class multi_array_iterator {
public:
using container_type = std::vector<size_t>;
multi_array_iterator(const std::array<buffer_info, N> &buffers,
const std::vector<size_t> &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] = static_cast<container_type::value_type>(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;
} else {
m_index[i] = 0;
}
}
return *this;
}
template <size_t K, class T> const 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 std::vector<size_t> &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 = static_cast<size_t>(*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, size_t &ndim, std::vector<size_t> &shape) {
ndim = std::accumulate(buffers.begin(), buffers.end(), size_t(0), [](size_t res, const buffer_info& buf) {
return std::max(res, buf.ndim);
});
shape.clear();
shape.resize(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) {
size_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) {
size_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 Func, typename Return, typename... Args>
struct vectorize_helper {
typename std::remove_reference<Func>::type f;
static constexpr size_t N = sizeof...(Args);
template <typename T>
explicit vectorize_helper(T&&f) : f(std::forward<T>(f)) { }
object operator()(array_t<Args, array::forcecast>... args) {
return run(args..., make_index_sequence<N>());
}
template <size_t ... Index> object run(array_t<Args, array::forcecast>&... args, index_sequence<Index...> index) {
/* Request buffers from all parameters */
std::array<buffer_info, N> buffers {{ args.request()... }};
/* Determine dimensions parameters of output array */
size_t ndim = 0;
std::vector<size_t> shape(0);
auto trivial = broadcast(buffers, ndim, shape);
size_t size = 1;
std::vector<size_t> strides(ndim);
if (ndim > 0) {
if (trivial == broadcast_trivial::f_trivial) {
strides[0] = sizeof(Return);
for (size_t i = 1; i < ndim; ++i) {
strides[i] = strides[i - 1] * shape[i - 1];
size *= shape[i - 1];
}
size *= shape[ndim - 1];
}
else {
strides[ndim-1] = sizeof(Return);
for (size_t i = ndim - 1; i > 0; --i) {
strides[i - 1] = strides[i] * shape[i];
size *= shape[i];
}
size *= shape[0];
}
}
if (size == 1)
return cast(f(*reinterpret_cast<Args *>(buffers[Index].ptr)...));
array_t<Return> result(shape, strides);
auto buf = result.request();
auto output = (Return *) buf.ptr;
/* Call the function */
if (trivial == broadcast_trivial::non_trivial) {
apply_broadcast<Index...>(buffers, buf, index);
} else {
for (size_t i = 0; i < size; ++i)
output[i] = f((reinterpret_cast<Args *>(buffers[Index].ptr)[buffers[Index].size == 1 ? 0 : i])...);
}
return result;
}
template <size_t... Index>
void apply_broadcast(const std::array<buffer_info, N> &buffers,
buffer_info &output, index_sequence<Index...>) {
using input_iterator = multi_array_iterator<N>;
using output_iterator = array_iterator<Return>;
input_iterator input_iter(buffers, output.shape);
output_iterator output_end = array_end<Return>(output);
for (output_iterator iter = array_begin<Return>(output);
iter != output_end; ++iter, ++input_iter) {
*iter = f((input_iter.template data<Index, Args>())...);
}
}
};
template <typename T, int Flags> struct handle_type_name<array_t<T, Flags>> {
static PYBIND11_DESCR name() {
return _("numpy.ndarray[") + npy_format_descriptor<T>::name() + _("]");
}
};
NAMESPACE_END(detail)
template <typename Func, typename Return, typename... Args>
detail::vectorize_helper<Func, Return, Args...>
vectorize(const Func &f, Return (*) (Args ...)) {
return detail::vectorize_helper<Func, Return, Args...>(f);
}
template <typename Return, typename... Args>
detail::vectorize_helper<Return (*) (Args ...), Return, Args...>
vectorize(Return (*f) (Args ...)) {
return vectorize<Return (*) (Args ...), Return, Args...>(f, f);
}
template <typename Func, typename FuncType = typename detail::remove_class<decltype(&std::remove_reference<Func>::type::operator())>::type>
auto vectorize(Func &&f) -> decltype(
vectorize(std::forward<Func>(f), (FuncType *) nullptr)) {
return vectorize(std::forward<Func>(f), (FuncType *) nullptr);
}
NAMESPACE_END(pybind11)
#if defined(_MSC_VER)
#pragma warning(pop)
#endif