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R9490/homework4/pybind11/tests/test_numpy_vectorize.cpp
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test_numpy_vectorize.cpp
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/*
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);
});
}
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