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94 lines
3.7 KiB
94 lines
3.7 KiB
/*
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tests/test_numpy_vectorize.cpp -- auto-vectorize functions over NumPy array
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arguments
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Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
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All rights reserved. Use of this source code is governed by a
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BSD-style license that can be found in the LICENSE file.
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*/
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#include "pybind11_tests.h"
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#include <pybind11/numpy.h>
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double my_func(int x, float y, double z) {
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py::print("my_func(x:int={}, y:float={:.0f}, z:float={:.0f})"_s.format(x, y, z));
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return (float) x*y*z;
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}
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TEST_SUBMODULE(numpy_vectorize, m) {
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try { py::module_::import("numpy"); }
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catch (...) { return; }
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// test_vectorize, test_docs, test_array_collapse
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// Vectorize all arguments of a function (though non-vector arguments are also allowed)
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m.def("vectorized_func", py::vectorize(my_func));
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// Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the vectorization)
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m.def("vectorized_func2",
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[](py::array_t<int> x, py::array_t<float> y, float z) {
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return py::vectorize([z](int x, float y) { return my_func(x, y, z); })(x, y);
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}
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);
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// Vectorize a complex-valued function
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m.def("vectorized_func3", py::vectorize(
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[](std::complex<double> c) { return c * std::complex<double>(2.f); }
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));
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// test_type_selection
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// NumPy function which only accepts specific data types
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m.def("selective_func", [](py::array_t<int, py::array::c_style>) { return "Int branch taken."; });
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m.def("selective_func", [](py::array_t<float, py::array::c_style>) { return "Float branch taken."; });
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m.def("selective_func", [](py::array_t<std::complex<float>, py::array::c_style>) { return "Complex float branch taken."; });
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// test_passthrough_arguments
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// Passthrough test: references and non-pod types should be automatically passed through (in the
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// function definition below, only `b`, `d`, and `g` are vectorized):
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struct NonPODClass {
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NonPODClass(int v) : value{v} {}
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int value;
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};
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py::class_<NonPODClass>(m, "NonPODClass")
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.def(py::init<int>())
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.def_readwrite("value", &NonPODClass::value);
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m.def("vec_passthrough", py::vectorize(
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[](double *a, double b, py::array_t<double> c, const int &d, int &e, NonPODClass f, const double g) {
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return *a + b + c.at(0) + d + e + f.value + g;
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}
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));
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// test_method_vectorization
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struct VectorizeTestClass {
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VectorizeTestClass(int v) : value{v} {};
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float method(int x, float y) { return y + (float) (x + value); }
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int value = 0;
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};
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py::class_<VectorizeTestClass> vtc(m, "VectorizeTestClass");
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vtc .def(py::init<int>())
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.def_readwrite("value", &VectorizeTestClass::value);
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// Automatic vectorizing of methods
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vtc.def("method", py::vectorize(&VectorizeTestClass::method));
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// test_trivial_broadcasting
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// Internal optimization test for whether the input is trivially broadcastable:
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py::enum_<py::detail::broadcast_trivial>(m, "trivial")
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.value("f_trivial", py::detail::broadcast_trivial::f_trivial)
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.value("c_trivial", py::detail::broadcast_trivial::c_trivial)
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.value("non_trivial", py::detail::broadcast_trivial::non_trivial);
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m.def("vectorized_is_trivial", [](
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py::array_t<int, py::array::forcecast> arg1,
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py::array_t<float, py::array::forcecast> arg2,
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py::array_t<double, py::array::forcecast> arg3
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) {
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py::ssize_t ndim;
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std::vector<py::ssize_t> shape;
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std::array<py::buffer_info, 3> buffers {{ arg1.request(), arg2.request(), arg3.request() }};
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return py::detail::broadcast(buffers, ndim, shape);
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});
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m.def("add_to", py::vectorize([](NonPODClass& x, int a) { x.value += a; }));
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}
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