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Wenzel Jakobd4258ba2015-07-26 16:33:49 +02001/*
Dean Moldovana0c1ccf2016-08-12 13:50:00 +02002 tests/test_numpy_vectorize.cpp -- auto-vectorize functions over NumPy array
Wenzel Jakoba576e6a2015-07-29 17:51:54 +02003 arguments
Wenzel Jakobd4258ba2015-07-26 16:33:49 +02004
Wenzel Jakob8cb6cb32016-04-17 20:21:41 +02005 Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
Wenzel Jakobd4258ba2015-07-26 16:33:49 +02006
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
Dean Moldovana0c1ccf2016-08-12 13:50:00 +020011#include "pybind11_tests.h"
Wenzel Jakob8f4eb002015-10-15 18:13:33 +020012#include <pybind11/numpy.h>
Wenzel Jakobd4258ba2015-07-26 16:33:49 +020013
14double my_func(int x, float y, double z) {
Dean Moldovan81511be2016-09-07 00:50:10 +020015 py::print("my_func(x:int={}, y:float={:.0f}, z:float={:.0f})"_s.format(x, y, z));
Boris Schäling20ee9352016-05-28 12:26:18 +020016 return (float) x*y*z;
Wenzel Jakobd4258ba2015-07-26 16:33:49 +020017}
18
Wenzel Jakob43398a82015-07-28 16:12:20 +020019std::complex<double> my_func3(std::complex<double> c) {
20 return c * std::complex<double>(2.f);
21}
22
Jason Rhinelander52f4be82016-09-03 14:54:22 -040023test_initializer numpy_vectorize([](py::module &m) {
Wenzel Jakoba576e6a2015-07-29 17:51:54 +020024 // Vectorize all arguments of a function (though non-vector arguments are also allowed)
Wenzel Jakobd4258ba2015-07-26 16:33:49 +020025 m.def("vectorized_func", py::vectorize(my_func));
Wenzel Jakoba576e6a2015-07-29 17:51:54 +020026
Wenzel Jakobd4258ba2015-07-26 16:33:49 +020027 // Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the vectorization)
28 m.def("vectorized_func2",
Wenzel Jakobb50872a2015-10-13 17:38:22 +020029 [](py::array_t<int> x, py::array_t<float> y, float z) {
Wenzel Jakobd4258ba2015-07-26 16:33:49 +020030 return py::vectorize([z](int x, float y) { return my_func(x, y, z); })(x, y);
31 }
32 );
Wenzel Jakoba576e6a2015-07-29 17:51:54 +020033
34 // Vectorize a complex-valued function
Wenzel Jakob43398a82015-07-28 16:12:20 +020035 m.def("vectorized_func3", py::vectorize(my_func3));
Wenzel Jakobb47a9de2016-05-19 16:02:09 +020036
37 /// Numpy function which only accepts specific data types
Dean Moldovan665e8802016-08-12 22:28:31 +020038 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."; });
Jason Rhinelanderae5a8f72017-03-15 00:57:56 -030041
42
43 // Internal optimization test for whether the input is trivially broadcastable:
Jason Rhinelanderb0292c12017-03-18 21:11:59 -030044 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);
Jason Rhinelanderae5a8f72017-03-15 00:57:56 -030048 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 ) {
Cris Luengo30d43c42017-04-14 14:33:44 -060053 ssize_t ndim;
54 std::vector<ssize_t> shape;
Jason Rhinelanderae5a8f72017-03-15 00:57:56 -030055 std::array<py::buffer_info, 3> buffers {{ arg1.request(), arg2.request(), arg3.request() }};
56 return py::detail::broadcast(buffers, ndim, shape);
57 });
Jason Rhinelander52f4be82016-09-03 14:54:22 -040058});