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Diffstat (limited to 'src/boost/libs/python/example/numpy/wrap.cpp')
-rw-r--r-- | src/boost/libs/python/example/numpy/wrap.cpp | 135 |
1 files changed, 135 insertions, 0 deletions
diff --git a/src/boost/libs/python/example/numpy/wrap.cpp b/src/boost/libs/python/example/numpy/wrap.cpp new file mode 100644 index 000000000..33f71c85a --- /dev/null +++ b/src/boost/libs/python/example/numpy/wrap.cpp @@ -0,0 +1,135 @@ +// Copyright Jim Bosch 2011-2012. +// Distributed under the Boost Software License, Version 1.0. +// (See accompanying file LICENSE_1_0.txt or copy at +// http://www.boost.org/LICENSE_1_0.txt) + +/** + * A simple example showing how to wrap a couple of C++ functions that + * operate on 2-d arrays into Python functions that take NumPy arrays + * as arguments. + * + * If you find have a lot of such functions to wrap, you may want to + * create a C++ array type (or use one of the many existing C++ array + * libraries) that maps well to NumPy arrays and create Boost.Python + * converters. There's more work up front than the approach here, + * but much less boilerplate per function. See the "Gaussian" example + * included with Boost.NumPy for an example of custom converters, or + * take a look at the "ndarray" project on GitHub for a more complete, + * high-level solution. + * + * Note that we're using embedded Python here only to make a convenient + * self-contained example; you could just as easily put the wrappers + * in a regular C++-compiled module and imported them in regular + * Python. Again, see the Gaussian demo for an example. + */ + +#include <boost/python/numpy.hpp> +#include <boost/scoped_array.hpp> +#include <iostream> + +namespace p = boost::python; +namespace np = boost::python::numpy; + +// This is roughly the most efficient way to write a C/C++ function that operates +// on a 2-d NumPy array - operate directly on the array by incrementing a pointer +// with the strides. +void fill1(double * array, int rows, int cols, int row_stride, int col_stride) { + double * row_iter = array; + double n = 0.0; // just a counter we'll fill the array with. + for (int i = 0; i < rows; ++i, row_iter += row_stride) { + double * col_iter = row_iter; + for (int j = 0; j < cols; ++j, col_iter += col_stride) { + *col_iter = ++n; + } + } +} + +// Here's a simple wrapper function for fill1. It requires that the passed +// NumPy array be exactly what we're looking for - no conversion from nested +// sequences or arrays with other data types, because we want to modify it +// in-place. +void wrap_fill1(np::ndarray const & array) { + if (array.get_dtype() != np::dtype::get_builtin<double>()) { + PyErr_SetString(PyExc_TypeError, "Incorrect array data type"); + p::throw_error_already_set(); + } + if (array.get_nd() != 2) { + PyErr_SetString(PyExc_TypeError, "Incorrect number of dimensions"); + p::throw_error_already_set(); + } + fill1(reinterpret_cast<double*>(array.get_data()), + array.shape(0), array.shape(1), + array.strides(0) / sizeof(double), array.strides(1) / sizeof(double)); +} + +// Another fill function that takes a double**. This is less efficient, because +// it's not the native NumPy data layout, but it's common enough in C/C++ that +// it's worth its own example. This time we don't pass the strides, and instead +// in wrap_fill2 we'll require the C_CONTIGUOUS bitflag, which guarantees that +// the column stride is 1 and the row stride is the number of columns. That +// restricts the arrays that can be passed to fill2 (it won't work on most +// subarray views or transposes, for instance). +void fill2(double ** array, int rows, int cols) { + double n = 0.0; // just a counter we'll fill the array with. + for (int i = 0; i < rows; ++i) { + for (int j = 0; j < cols; ++j) { + array[i][j] = ++n; + } + } +} +// Here's the wrapper for fill2; it's a little more complicated because we need +// to check the flags and create the array of pointers. +void wrap_fill2(np::ndarray const & array) { + if (array.get_dtype() != np::dtype::get_builtin<double>()) { + PyErr_SetString(PyExc_TypeError, "Incorrect array data type"); + p::throw_error_already_set(); + } + if (array.get_nd() != 2) { + PyErr_SetString(PyExc_TypeError, "Incorrect number of dimensions"); + p::throw_error_already_set(); + } + if (!(array.get_flags() & np::ndarray::C_CONTIGUOUS)) { + PyErr_SetString(PyExc_TypeError, "Array must be row-major contiguous"); + p::throw_error_already_set(); + } + double * iter = reinterpret_cast<double*>(array.get_data()); + int rows = array.shape(0); + int cols = array.shape(1); + boost::scoped_array<double*> ptrs(new double*[rows]); + for (int i = 0; i < rows; ++i, iter += cols) { + ptrs[i] = iter; + } + fill2(ptrs.get(), array.shape(0), array.shape(1)); +} + +BOOST_PYTHON_MODULE(example) { + np::initialize(); // have to put this in any module that uses Boost.NumPy + p::def("fill1", wrap_fill1); + p::def("fill2", wrap_fill2); +} + +int main(int argc, char **argv) +{ + // This line makes our module available to the embedded Python intepreter. +# if PY_VERSION_HEX >= 0x03000000 + PyImport_AppendInittab("example", &PyInit_example); +# else + PyImport_AppendInittab("example", &initexample); +# endif + // Initialize the Python runtime. + Py_Initialize(); + + PyRun_SimpleString( + "import example\n" + "import numpy\n" + "z1 = numpy.zeros((5,6), dtype=float)\n" + "z2 = numpy.zeros((4,3), dtype=float)\n" + "example.fill1(z1)\n" + "example.fill2(z2)\n" + "print z1\n" + "print z2\n" + ); + Py_Finalize(); +} + + |