added embedded repo pybind11.

python/pybind11/tests/test_numpy_vectorize.cpp

@@ -0,0 +1,89 @@
+/*
+    tests/test_numpy_vectorize.cpp -- auto-vectorize functions over NumPy array
+    arguments
+
+    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.
+*/
+
+#include "pybind11_tests.h"
+#include <pybind11/numpy.h>
+
+double my_func(int x, float y, double z) {
+    py::print("my_func(x:int={}, y:float={:.0f}, z:float={:.0f})"_s.format(x, y, z));
+    return (float) x*y*z;
+}
+
+TEST_SUBMODULE(numpy_vectorize, m) {
+    try { py::module::import("numpy"); }
+    catch (...) { return; }
+
+    // test_vectorize, test_docs, test_array_collapse
+    // Vectorize all arguments of a function (though non-vector arguments are also allowed)
+    m.def("vectorized_func", py::vectorize(my_func));
+
+    // Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the vectorization)
+    m.def("vectorized_func2",
+        [](py::array_t<int> x, py::array_t<float> y, float z) {
+            return py::vectorize([z](int x, float y) { return my_func(x, y, z); })(x, y);
+        }
+    );
+
+    // Vectorize a complex-valued function
+    m.def("vectorized_func3", py::vectorize(
+        [](std::complex<double> c) { return c * std::complex<double>(2.f); }
+    ));
+
+    // test_type_selection
+    // Numpy function which only accepts specific data types
+    m.def("selective_func", [](py::array_t<int, py::array::c_style>) { return "Int branch taken."; });
+    m.def("selective_func", [](py::array_t<float, py::array::c_style>) { return "Float branch taken."; });
+    m.def("selective_func", [](py::array_t<std::complex<float>, py::array::c_style>) { return "Complex float branch taken."; });
+
+
+    // test_passthrough_arguments
+    // Passthrough test: references and non-pod types should be automatically passed through (in the
+    // function definition below, only `b`, `d`, and `g` are vectorized):
+    struct NonPODClass {
+        NonPODClass(int v) : value{v} {}
+        int value;
+    };
+    py::class_<NonPODClass>(m, "NonPODClass").def(py::init<int>());
+    m.def("vec_passthrough", py::vectorize(
+        [](double *a, double b, py::array_t<double> c, const int &d, int &e, NonPODClass f, const double g) {
+            return *a + b + c.at(0) + d + e + f.value + g;
+        }
+    ));
+
+    // test_method_vectorization
+    struct VectorizeTestClass {
+        VectorizeTestClass(int v) : value{v} {};
+        float method(int x, float y) { return y + (float) (x + value); }
+        int value = 0;
+    };
+    py::class_<VectorizeTestClass> vtc(m, "VectorizeTestClass");
+    vtc .def(py::init<int>())
+        .def_readwrite("value", &VectorizeTestClass::value);
+
+    // Automatic vectorizing of methods
+    vtc.def("method", py::vectorize(&VectorizeTestClass::method));
+
+    // test_trivial_broadcasting
+    // Internal optimization test for whether the input is trivially broadcastable:
+    py::enum_<py::detail::broadcast_trivial>(m, "trivial")
+        .value("f_trivial", py::detail::broadcast_trivial::f_trivial)
+        .value("c_trivial", py::detail::broadcast_trivial::c_trivial)
+        .value("non_trivial", py::detail::broadcast_trivial::non_trivial);
+    m.def("vectorized_is_trivial", [](
+                py::array_t<int, py::array::forcecast> arg1,
+                py::array_t<float, py::array::forcecast> arg2,
+                py::array_t<double, py::array::forcecast> arg3
+                ) {
+        ssize_t ndim;
+        std::vector<ssize_t> shape;
+        std::array<py::buffer_info, 3> buffers {{ arg1.request(), arg2.request(), arg3.request() }};
+        return py::detail::broadcast(buffers, ndim, shape);
+    });
+}