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665e8804f3
The C++ part of the test code is modified to achieve this. As a result,
this kind of test:
```python
with capture:
kw_func1(5, y=10)
assert capture == "kw_func(x=5, y=10)"
```
can be replaced with a simple:
`assert kw_func1(5, y=10) == "x=5, y=10"`
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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std::cout << "my_func(x:int=" << x << ", y:float=" << y << ", z:float=" << z << ")" << std::endl;
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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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void init_ex_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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