206 lines
6.7 KiB
Plaintext
206 lines
6.7 KiB
Plaintext
// Copyright Jim Bosch 2010-2012.
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// Copyright Stefan Seefeld 2016.
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// Distributed under the Boost Software License, Version 1.0.
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// (See accompanying file LICENSE_1_0.txt or copy at
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// http://www.boost.org/LICENSE_1_0.txt)
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#ifndef boost_python_numpy_ufunc_hpp_
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#define boost_python_numpy_ufunc_hpp_
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/**
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* @brief Utilities to create ufunc-like broadcasting functions out of C++ functors.
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*/
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#include <boost/python.hpp>
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#include <boost/python/numpy/numpy_object_mgr_traits.hpp>
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#include <boost/python/numpy/dtype.hpp>
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#include <boost/python/numpy/ndarray.hpp>
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namespace boost { namespace python { namespace numpy {
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/**
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* @brief A boost.python "object manager" (subclass of object) for PyArray_MultiIter.
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*
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* multi_iter is a Python object, but a very low-level one. It should generally only be used
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* in loops of the form:
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* @code
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* while (iter.not_done()) {
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* ...
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* iter.next();
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* }
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* @endcode
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*
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* @todo I can't tell if this type is exposed in Python anywhere; if it is, we should use that name.
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* It's more dangerous than most object managers, however - maybe it actually belongs in
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* a detail namespace?
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*/
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class multi_iter : public object
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{
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public:
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BOOST_PYTHON_FORWARD_OBJECT_CONSTRUCTORS(multi_iter, object);
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/// @brief Increment the iterator.
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void next();
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/// @brief Check if the iterator is at its end.
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bool not_done() const;
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/// @brief Return a pointer to the element of the nth broadcasted array.
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char * get_data(int n) const;
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/// @brief Return the number of dimensions of the broadcasted array expression.
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int get_nd() const;
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/// @brief Return the shape of the broadcasted array expression as an array of integers.
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Py_intptr_t const * get_shape() const;
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/// @brief Return the shape of the broadcasted array expression in the nth dimension.
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Py_intptr_t shape(int n) const;
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};
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/// @brief Construct a multi_iter over a single sequence or scalar object.
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multi_iter make_multi_iter(object const & a1);
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/// @brief Construct a multi_iter by broadcasting two objects.
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multi_iter make_multi_iter(object const & a1, object const & a2);
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/// @brief Construct a multi_iter by broadcasting three objects.
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multi_iter make_multi_iter(object const & a1, object const & a2, object const & a3);
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/**
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* @brief Helps wrap a C++ functor taking a single scalar argument as a broadcasting ufunc-like
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* Python object.
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*
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* Typical usage looks like this:
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* @code
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* struct TimesPI
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* {
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* typedef double argument_type;
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* typedef double result_type;
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* double operator()(double input) const { return input * M_PI; }
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* };
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*
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* BOOST_PYTHON_MODULE(example)
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* {
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* class_< TimesPI >("TimesPI")
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* .def("__call__", unary_ufunc<TimesPI>::make());
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* }
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* @endcode
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*
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*/
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template <typename TUnaryFunctor,
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typename TArgument=typename TUnaryFunctor::argument_type,
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typename TResult=typename TUnaryFunctor::result_type>
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struct unary_ufunc
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{
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/**
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* @brief A C++ function with object arguments that broadcasts its arguments before
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* passing them to the underlying C++ functor.
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*/
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static object call(TUnaryFunctor & self, object const & input, object const & output)
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{
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dtype in_dtype = dtype::get_builtin<TArgument>();
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dtype out_dtype = dtype::get_builtin<TResult>();
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ndarray in_array = from_object(input, in_dtype, ndarray::ALIGNED);
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ndarray out_array = (output != object()) ?
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from_object(output, out_dtype, ndarray::ALIGNED | ndarray::WRITEABLE)
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: zeros(in_array.get_nd(), in_array.get_shape(), out_dtype);
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multi_iter iter = make_multi_iter(in_array, out_array);
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while (iter.not_done())
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{
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TArgument * argument = reinterpret_cast<TArgument*>(iter.get_data(0));
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TResult * result = reinterpret_cast<TResult*>(iter.get_data(1));
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*result = self(*argument);
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iter.next();
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}
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return out_array.scalarize();
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}
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/**
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* @brief Construct a boost.python function object from call() with reasonable keyword names.
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*
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* Users will often want to specify their own keyword names with the same signature, but this
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* is a convenient shortcut.
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*/
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static object make()
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{
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return make_function(call, default_call_policies(), (arg("input"), arg("output")=object()));
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}
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};
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/**
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* @brief Helps wrap a C++ functor taking a pair of scalar arguments as a broadcasting ufunc-like
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* Python object.
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*
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* Typical usage looks like this:
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* @code
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* struct CosSum
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* {
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* typedef double first_argument_type;
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* typedef double second_argument_type;
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* typedef double result_type;
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* double operator()(double input1, double input2) const { return std::cos(input1 + input2); }
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* };
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*
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* BOOST_PYTHON_MODULE(example)
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* {
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* class_< CosSum >("CosSum")
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* .def("__call__", binary_ufunc<CosSum>::make());
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* }
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* @endcode
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*
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*/
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template <typename TBinaryFunctor,
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typename TArgument1=typename TBinaryFunctor::first_argument_type,
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typename TArgument2=typename TBinaryFunctor::second_argument_type,
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typename TResult=typename TBinaryFunctor::result_type>
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struct binary_ufunc
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{
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static object
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call(TBinaryFunctor & self, object const & input1, object const & input2,
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object const & output)
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{
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dtype in1_dtype = dtype::get_builtin<TArgument1>();
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dtype in2_dtype = dtype::get_builtin<TArgument2>();
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dtype out_dtype = dtype::get_builtin<TResult>();
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ndarray in1_array = from_object(input1, in1_dtype, ndarray::ALIGNED);
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ndarray in2_array = from_object(input2, in2_dtype, ndarray::ALIGNED);
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multi_iter iter = make_multi_iter(in1_array, in2_array);
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ndarray out_array = (output != object())
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? from_object(output, out_dtype, ndarray::ALIGNED | ndarray::WRITEABLE)
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: zeros(iter.get_nd(), iter.get_shape(), out_dtype);
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iter = make_multi_iter(in1_array, in2_array, out_array);
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while (iter.not_done())
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{
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TArgument1 * argument1 = reinterpret_cast<TArgument1*>(iter.get_data(0));
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TArgument2 * argument2 = reinterpret_cast<TArgument2*>(iter.get_data(1));
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TResult * result = reinterpret_cast<TResult*>(iter.get_data(2));
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*result = self(*argument1, *argument2);
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iter.next();
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}
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return out_array.scalarize();
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}
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static object make()
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{
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return make_function(call, default_call_policies(),
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(arg("input1"), arg("input2"), arg("output")=object()));
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}
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};
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} // namespace boost::python::numpy
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namespace converter
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{
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NUMPY_OBJECT_MANAGER_TRAITS(numpy::multi_iter);
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}}} // namespace boost::python::converter
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#endif
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