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
my_func(int x,float y,double z)14 double 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
TEST_SUBMODULE(numpy_vectorize,m)19 TEST_SUBMODULE(numpy_vectorize, m) {
20 try { py::module_::import("numpy"); }
21 catch (...) { return; }
22
23 // test_vectorize, test_docs, test_array_collapse
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(
36 [](std::complex<double> c) { return c * std::complex<double>(2.f); }
37 ));
38
39 // test_type_selection
40 // NumPy function which only accepts specific data types
41 m.def("selective_func", [](py::array_t<int, py::array::c_style>) { return "Int branch taken."; });
42 m.def("selective_func", [](py::array_t<float, py::array::c_style>) { return "Float branch taken."; });
43 m.def("selective_func", [](py::array_t<std::complex<float>, py::array::c_style>) { return "Complex float branch taken."; });
44
45
46 // test_passthrough_arguments
47 // Passthrough test: references and non-pod types should be automatically passed through (in the
48 // function definition below, only `b`, `d`, and `g` are vectorized):
49 struct NonPODClass {
50 NonPODClass(int v) : value{v} {}
51 int value;
52 };
53 py::class_<NonPODClass>(m, "NonPODClass")
54 .def(py::init<int>())
55 .def_readwrite("value", &NonPODClass::value);
56 m.def("vec_passthrough", py::vectorize(
57 [](double *a, double b, py::array_t<double> c, const int &d, int &e, NonPODClass f, const double g) {
58 return *a + b + c.at(0) + d + e + f.value + g;
59 }
60 ));
61
62 // test_method_vectorization
63 struct VectorizeTestClass {
64 VectorizeTestClass(int v) : value{v} {};
65 float method(int x, float y) { return y + (float) (x + value); }
66 int value = 0;
67 };
68 py::class_<VectorizeTestClass> vtc(m, "VectorizeTestClass");
69 vtc .def(py::init<int>())
70 .def_readwrite("value", &VectorizeTestClass::value);
71
72 // Automatic vectorizing of methods
73 vtc.def("method", py::vectorize(&VectorizeTestClass::method));
74
75 // test_trivial_broadcasting
76 // Internal optimization test for whether the input is trivially broadcastable:
77 py::enum_<py::detail::broadcast_trivial>(m, "trivial")
78 .value("f_trivial", py::detail::broadcast_trivial::f_trivial)
79 .value("c_trivial", py::detail::broadcast_trivial::c_trivial)
80 .value("non_trivial", py::detail::broadcast_trivial::non_trivial);
81 m.def("vectorized_is_trivial", [](
82 py::array_t<int, py::array::forcecast> arg1,
83 py::array_t<float, py::array::forcecast> arg2,
84 py::array_t<double, py::array::forcecast> arg3
85 ) {
86 py::ssize_t ndim;
87 std::vector<py::ssize_t> shape;
88 std::array<py::buffer_info, 3> buffers {{ arg1.request(), arg2.request(), arg3.request() }};
89 return py::detail::broadcast(buffers, ndim, shape);
90 });
91
92 m.def("add_to", py::vectorize([](NonPODClass& x, int a) { x.value += a; }));
93 }
94