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Merge pull request #472 from aldanor/feature/shared-dtypes
Support for sharing dtypes across extensions + public shared data API
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commit
0a9ef9c300
@ -149,6 +149,25 @@ accessed by multiple extension modules:
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...
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};
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Note also that it is possible (although would rarely be required) to share arbitrary
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C++ objects between extension modules at runtime. Internal library data is shared
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between modules using capsule machinery [#f6]_ which can be also utilized for
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storing, modifying and accessing user-defined data. Note that an extension module
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will "see" other extensions' data if and only if they were built with the same
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pybind11 version. Consider the following example:
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.. code-block:: cpp
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auto data = (MyData *) py::get_shared_data("mydata");
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if (!data)
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data = (MyData *) py::set_shared_data("mydata", new MyData(42));
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If the above snippet was used in several separately compiled extension modules,
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the first one to be imported would create a ``MyData`` instance and associate
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a ``"mydata"`` key with a pointer to it. Extensions that are imported later
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would be then able to access the data behind the same pointer.
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.. [#f6] https://docs.python.org/3/extending/extending.html#using-capsules
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Generating documentation using Sphinx
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@ -323,6 +323,7 @@ struct internals {
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std::unordered_set<std::pair<const PyObject *, const char *>, overload_hash> inactive_overload_cache;
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std::unordered_map<std::type_index, std::vector<bool (*)(PyObject *, void *&)>> direct_conversions;
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std::forward_list<void (*) (std::exception_ptr)> registered_exception_translators;
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std::unordered_map<std::string, void *> shared_data; // Custom data to be shared across extensions
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#if defined(WITH_THREAD)
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decltype(PyThread_create_key()) tstate = 0; // Usually an int but a long on Cygwin64 with Python 3.x
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PyInterpreterState *istate = nullptr;
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@ -427,6 +428,35 @@ inline void ignore_unused(const int *) { }
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NAMESPACE_END(detail)
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/// Returns a named pointer that is shared among all extension modules (using the same
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/// pybind11 version) running in the current interpreter. Names starting with underscores
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/// are reserved for internal usage. Returns `nullptr` if no matching entry was found.
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inline PYBIND11_NOINLINE void* get_shared_data(const std::string& name) {
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auto& internals = detail::get_internals();
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auto it = internals.shared_data.find(name);
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return it != internals.shared_data.end() ? it->second : nullptr;
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}
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/// Set the shared data that can be later recovered by `get_shared_data()`.
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inline PYBIND11_NOINLINE void *set_shared_data(const std::string& name, void *data) {
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detail::get_internals().shared_data[name] = data;
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return data;
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}
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/// Returns a typed reference to a shared data entry (by using `get_shared_data()`) if
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/// such entry exists. Otherwise, a new object of default-constructible type `T` is
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/// added to the shared data under the given name and a reference to it is returned.
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template<typename T> T& get_or_create_shared_data(const std::string& name) {
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auto& internals = detail::get_internals();
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auto it = internals.shared_data.find(name);
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T* ptr = (T*) (it != internals.shared_data.end() ? it->second : nullptr);
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if (!ptr) {
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ptr = new T();
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internals.shared_data[name] = ptr;
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}
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return *ptr;
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}
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/// Fetch and hold an error which was already set in Python
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class error_already_set : public std::runtime_error {
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public:
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@ -21,6 +21,7 @@
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#include <initializer_list>
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#include <functional>
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#include <utility>
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#include <typeindex>
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#if defined(_MSC_VER)
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# pragma warning(push)
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@ -72,6 +73,39 @@ struct PyVoidScalarObject_Proxy {
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PyObject *base;
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};
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struct numpy_type_info {
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PyObject* dtype_ptr;
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std::string format_str;
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};
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struct numpy_internals {
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std::unordered_map<std::type_index, numpy_type_info> registered_dtypes;
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numpy_type_info *get_type_info(const std::type_info& tinfo, bool throw_if_missing = true) {
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auto it = registered_dtypes.find(std::type_index(tinfo));
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if (it != registered_dtypes.end())
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return &(it->second);
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if (throw_if_missing)
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pybind11_fail(std::string("NumPy type info missing for ") + tinfo.name());
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return nullptr;
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}
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template<typename T> numpy_type_info *get_type_info(bool throw_if_missing = true) {
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return get_type_info(typeid(typename std::remove_cv<T>::type), throw_if_missing);
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}
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};
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inline PYBIND11_NOINLINE void load_numpy_internals(numpy_internals* &ptr) {
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ptr = &get_or_create_shared_data<numpy_internals>("_numpy_internals");
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}
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inline numpy_internals& get_numpy_internals() {
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static numpy_internals* ptr = nullptr;
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if (!ptr)
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load_numpy_internals(ptr);
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return *ptr;
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}
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struct npy_api {
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enum constants {
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NPY_C_CONTIGUOUS_ = 0x0001,
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@ -656,35 +690,25 @@ struct field_descriptor {
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dtype descr;
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};
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template <typename T>
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struct npy_format_descriptor<T, enable_if_t<is_pod_struct<T>::value>> {
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static PYBIND11_DESCR name() { return _("struct"); }
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static pybind11::dtype dtype() {
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if (!dtype_ptr)
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pybind11_fail("NumPy: unsupported buffer format!");
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return object(dtype_ptr, true);
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}
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static std::string format() {
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if (!dtype_ptr)
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pybind11_fail("NumPy: unsupported buffer format!");
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return format_str;
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}
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static void register_dtype(std::initializer_list<field_descriptor> fields) {
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if (dtype_ptr)
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inline PYBIND11_NOINLINE void register_structured_dtype(
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const std::initializer_list<field_descriptor>& fields,
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const std::type_info& tinfo, size_t itemsize,
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bool (*direct_converter)(PyObject *, void *&))
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{
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auto& numpy_internals = get_numpy_internals();
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if (numpy_internals.get_type_info(tinfo, false))
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pybind11_fail("NumPy: dtype is already registered");
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list names, formats, offsets;
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for (auto field : fields) {
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if (!field.descr)
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pybind11_fail("NumPy: unsupported field dtype");
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pybind11_fail(std::string("NumPy: unsupported field dtype: `") +
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field.name + "` @ " + tinfo.name());
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names.append(PYBIND11_STR_TYPE(field.name));
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formats.append(field.descr);
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offsets.append(pybind11::int_(field.offset));
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}
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dtype_ptr = pybind11::dtype(names, formats, offsets, sizeof(T)).release().ptr();
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auto dtype_ptr = pybind11::dtype(names, formats, offsets, itemsize).release().ptr();
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// There is an existing bug in NumPy (as of v1.11): trailing bytes are
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// not encoded explicitly into the format string. This will supposedly
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@ -695,9 +719,7 @@ struct npy_format_descriptor<T, enable_if_t<is_pod_struct<T>::value>> {
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// strings and will just do it ourselves.
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std::vector<field_descriptor> ordered_fields(fields);
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std::sort(ordered_fields.begin(), ordered_fields.end(),
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[](const field_descriptor &a, const field_descriptor &b) {
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return a.offset < b.offset;
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});
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[](const field_descriptor &a, const field_descriptor &b) { return a.offset < b.offset; });
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size_t offset = 0;
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std::ostringstream oss;
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oss << "T{";
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@ -708,47 +730,60 @@ struct npy_format_descriptor<T, enable_if_t<is_pod_struct<T>::value>> {
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oss << '=' << field.format << ':' << field.name << ':';
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offset = field.offset + field.size;
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}
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if (sizeof(T) > offset)
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oss << (sizeof(T) - offset) << 'x';
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if (itemsize > offset)
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oss << (itemsize - offset) << 'x';
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oss << '}';
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format_str = oss.str();
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auto format_str = oss.str();
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// Sanity check: verify that NumPy properly parses our buffer format string
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auto& api = npy_api::get();
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auto arr = array(buffer_info(nullptr, sizeof(T), format(), 1));
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auto arr = array(buffer_info(nullptr, itemsize, format_str, 1));
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if (!api.PyArray_EquivTypes_(dtype_ptr, arr.dtype().ptr()))
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pybind11_fail("NumPy: invalid buffer descriptor!");
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register_direct_converter();
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auto tindex = std::type_index(tinfo);
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numpy_internals.registered_dtypes[tindex] = { dtype_ptr, format_str };
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get_internals().direct_conversions[tindex].push_back(direct_converter);
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}
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template <typename T>
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struct npy_format_descriptor<T, enable_if_t<is_pod_struct<T>::value>> {
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static PYBIND11_DESCR name() { return _("struct"); }
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static pybind11::dtype dtype() {
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return object(dtype_ptr(), true);
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}
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static std::string format() {
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static auto format_str = get_numpy_internals().get_type_info<T>(true)->format_str;
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return format_str;
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}
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static void register_dtype(const std::initializer_list<field_descriptor>& fields) {
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register_structured_dtype(fields, typeid(typename std::remove_cv<T>::type),
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sizeof(T), &direct_converter);
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}
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private:
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static std::string format_str;
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static PyObject* dtype_ptr;
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static PyObject* dtype_ptr() {
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static PyObject* ptr = get_numpy_internals().get_type_info<T>(true)->dtype_ptr;
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return ptr;
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}
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static bool direct_converter(PyObject *obj, void*& value) {
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auto& api = npy_api::get();
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if (!PyObject_TypeCheck(obj, api.PyVoidArrType_Type_))
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return false;
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if (auto descr = object(api.PyArray_DescrFromScalar_(obj), false)) {
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if (api.PyArray_EquivTypes_(dtype_ptr, descr.ptr())) {
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if (api.PyArray_EquivTypes_(dtype_ptr(), descr.ptr())) {
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value = ((PyVoidScalarObject_Proxy *) obj)->obval;
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return true;
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}
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}
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return false;
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}
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static void register_direct_converter() {
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get_internals().direct_conversions[std::type_index(typeid(T))].push_back(direct_converter);
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}
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};
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template <typename T>
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std::string npy_format_descriptor<T, enable_if_t<is_pod_struct<T>::value>>::format_str;
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template <typename T>
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PyObject* npy_format_descriptor<T, enable_if_t<is_pod_struct<T>::value>>::dtype_ptr = nullptr;
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#define PYBIND11_FIELD_DESCRIPTOR_EX(T, Field, Name) \
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::pybind11::detail::field_descriptor { \
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Name, offsetof(T, Field), sizeof(decltype(std::declval<T>().Field)), \
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@ -1,11 +1,20 @@
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import re
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import pytest
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with pytest.suppress(ImportError):
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import numpy as np
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simple_dtype = np.dtype({'names': ['x', 'y', 'z'],
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@pytest.fixture(scope='module')
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def simple_dtype():
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return np.dtype({'names': ['x', 'y', 'z'],
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'formats': ['?', 'u4', 'f4'],
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'offsets': [0, 4, 8]})
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packed_dtype = np.dtype([('x', '?'), ('y', 'u4'), ('z', 'f4')])
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@pytest.fixture(scope='module')
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def packed_dtype():
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return np.dtype([('x', '?'), ('y', 'u4'), ('z', 'f4')])
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def assert_equal(actual, expected_data, expected_dtype):
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@ -18,7 +27,7 @@ def test_format_descriptors():
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with pytest.raises(RuntimeError) as excinfo:
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get_format_unbound()
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assert 'unsupported buffer format' in str(excinfo.value)
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assert re.match('^NumPy type info missing for .*UnboundStruct.*$', str(excinfo.value))
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assert print_format_descriptors() == [
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"T{=?:x:3x=I:y:=f:z:}",
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@ -32,7 +41,7 @@ def test_format_descriptors():
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@pytest.requires_numpy
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def test_dtype():
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def test_dtype(simple_dtype):
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from pybind11_tests import print_dtypes, test_dtype_ctors, test_dtype_methods
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assert print_dtypes() == [
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@ -57,7 +66,7 @@ def test_dtype():
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@pytest.requires_numpy
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def test_recarray():
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def test_recarray(simple_dtype, packed_dtype):
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from pybind11_tests import (create_rec_simple, create_rec_packed, create_rec_nested,
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print_rec_simple, print_rec_packed, print_rec_nested,
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create_rec_partial, create_rec_partial_nested)
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