test_numpy_vectorize.cpp revision 12037 
1/*
2    tests/test_numpy_vectorize.cpp -- auto-vectorize functions over NumPy array
3    arguments
4
5    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
6
7    All rights reserved. Use of this source code is governed by a
8    BSD-style license that can be found in the LICENSE file.
9*/
10
11#include "pybind11_tests.h"
12#include <pybind11/numpy.h>
13
14double my_func(int x, float y, double z) {
15    py::print("my_func(x:int={}, y:float={:.0f}, z:float={:.0f})"_s.format(x, y, z));
16    return (float) x*y*z;
17}
18
19std::complex<double> my_func3(std::complex<double> c) {
20    return c * std::complex<double>(2.f);
21}
22
23test_initializer numpy_vectorize([](py::module &m) {
24    // Vectorize all arguments of a function (though non-vector arguments are also allowed)
25    m.def("vectorized_func", py::vectorize(my_func));
26
27    // Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the vectorization)
28    m.def("vectorized_func2",
29        [](py::array_t<int> x, py::array_t<float> y, float z) {
30            return py::vectorize([z](int x, float y) { return my_func(x, y, z); })(x, y);
31        }
32    );
33
34    // Vectorize a complex-valued function
35    m.def("vectorized_func3", py::vectorize(my_func3));
36
37    /// Numpy function which only accepts specific data types
38    m.def("selective_func", [](py::array_t<int, py::array::c_style>) { return "Int branch taken."; });
39    m.def("selective_func", [](py::array_t<float, py::array::c_style>) { return "Float branch taken."; });
40    m.def("selective_func", [](py::array_t<std::complex<float>, py::array::c_style>) { return "Complex float branch taken."; });
41
42
43    // Internal optimization test for whether the input is trivially broadcastable:
44    py::enum_<py::detail::broadcast_trivial>(m, "trivial")
45        .value("f_trivial", py::detail::broadcast_trivial::f_trivial)
46        .value("c_trivial", py::detail::broadcast_trivial::c_trivial)
47        .value("non_trivial", py::detail::broadcast_trivial::non_trivial);
48    m.def("vectorized_is_trivial", [](
49                py::array_t<int, py::array::forcecast> arg1,
50                py::array_t<float, py::array::forcecast> arg2,
51                py::array_t<double, py::array::forcecast> arg3
52                ) {
53        size_t ndim;
54        std::vector<size_t> shape;
55        std::array<py::buffer_info, 3> buffers {{ arg1.request(), arg2.request(), arg3.request() }};
56        return py::detail::broadcast(buffers, ndim, shape);
57    });
58});
59