/*

    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);

    });

}