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81511be341
With this change both C++ and Python write to sys.stdout which resolves the capture issues noted in #351. Therefore, the related workarounds are removed.
42 lines
1.6 KiB
C++
42 lines
1.6 KiB
C++
/*
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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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std::complex<double> my_func3(std::complex<double> c) {
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return c * std::complex<double>(2.f);
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}
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test_initializer numpy_vectorize([](py::module &m) {
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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(my_func3));
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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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});
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