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301 lines (266 loc) · 8.66 KB
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// Copyright Jim Bosch 2010-2012.
// Copyright Stefan Seefeld 2016.
// 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)
#define BOOST_PYTHON_NUMPY_INTERNAL
#include <boost/python/numpy/internal.hpp>
#include <boost/scoped_array.hpp>
namespace boost { namespace python {
namespace converter
{
NUMPY_OBJECT_MANAGER_TRAITS_IMPL(PyArray_Type, numpy::ndarray)
} // namespace boost::python::converter
namespace numpy
{
namespace detail
{
ndarray::bitflag numpy_to_bitflag(int const f)
{
ndarray::bitflag r = ndarray::NONE;
if (f & NPY_ARRAY_C_CONTIGUOUS) r = (r | ndarray::C_CONTIGUOUS);
if (f & NPY_ARRAY_F_CONTIGUOUS) r = (r | ndarray::F_CONTIGUOUS);
if (f & NPY_ARRAY_ALIGNED) r = (r | ndarray::ALIGNED);
if (f & NPY_ARRAY_WRITEABLE) r = (r | ndarray::WRITEABLE);
return r;
}
int bitflag_to_numpy(ndarray::bitflag f)
{
int r = 0;
if (f & ndarray::C_CONTIGUOUS) r |= NPY_ARRAY_C_CONTIGUOUS;
if (f & ndarray::F_CONTIGUOUS) r |= NPY_ARRAY_F_CONTIGUOUS;
if (f & ndarray::ALIGNED) r |= NPY_ARRAY_ALIGNED;
if (f & ndarray::WRITEABLE) r |= NPY_ARRAY_WRITEABLE;
return r;
}
bool is_c_contiguous(std::vector<Py_intptr_t> const & shape,
std::vector<Py_intptr_t> const & strides,
int itemsize)
{
std::vector<Py_intptr_t>::const_reverse_iterator j = strides.rbegin();
int total = itemsize;
for (std::vector<Py_intptr_t>::const_reverse_iterator i = shape.rbegin(); i != shape.rend(); ++i, ++j)
{
if (total != *j) return false;
total *= (*i);
}
return true;
}
bool is_f_contiguous(std::vector<Py_intptr_t> const & shape,
std::vector<Py_intptr_t> const & strides,
int itemsize)
{
std::vector<Py_intptr_t>::const_iterator j = strides.begin();
int total = itemsize;
for (std::vector<Py_intptr_t>::const_iterator i = shape.begin(); i != shape.end(); ++i, ++j)
{
if (total != *j) return false;
total *= (*i);
}
return true;
}
bool is_aligned(std::vector<Py_intptr_t> const & strides,
int itemsize)
{
for (std::vector<Py_intptr_t>::const_iterator i = strides.begin(); i != strides.end(); ++i)
{
if (*i % itemsize) return false;
}
return true;
}
inline PyArray_Descr * incref_dtype(dtype const & dt)
{
Py_INCREF(dt.ptr());
return reinterpret_cast<PyArray_Descr*>(dt.ptr());
}
ndarray from_data_impl(void * data,
dtype const & dt,
python::object const & shape,
python::object const & strides,
python::object const & owner,
bool writeable)
{
std::vector<Py_intptr_t> shape_(len(shape));
std::vector<Py_intptr_t> strides_(len(strides));
if (shape_.size() != strides_.size())
{
PyErr_SetString(PyExc_ValueError, "Length of shape and strides arrays do not match.");
python::throw_error_already_set();
}
for (std::size_t i = 0; i < shape_.size(); ++i)
{
shape_[i] = python::extract<Py_intptr_t>(shape[i]);
strides_[i] = python::extract<Py_intptr_t>(strides[i]);
}
return from_data_impl(data, dt, shape_, strides_, owner, writeable);
}
ndarray from_data_impl(void * data,
dtype const & dt,
std::vector<Py_intptr_t> const & shape,
std::vector<Py_intptr_t> const & strides,
python::object const & owner,
bool writeable)
{
if (shape.size() != strides.size())
{
PyErr_SetString(PyExc_ValueError, "Length of shape and strides arrays do not match.");
python::throw_error_already_set();
}
int itemsize = dt.get_itemsize();
int flags = 0;
if (writeable) flags |= NPY_ARRAY_WRITEABLE;
if (is_c_contiguous(shape, strides, itemsize)) flags |= NPY_ARRAY_C_CONTIGUOUS;
if (is_f_contiguous(shape, strides, itemsize)) flags |= NPY_ARRAY_F_CONTIGUOUS;
if (is_aligned(strides, itemsize)) flags |= NPY_ARRAY_ALIGNED;
ndarray r(python::detail::new_reference
(PyArray_NewFromDescr(&PyArray_Type,
incref_dtype(dt),
shape.size(),
const_cast<Py_intptr_t*>(&shape.front()),
const_cast<Py_intptr_t*>(&strides.front()),
data,
flags,
NULL)));
r.set_base(owner);
return r;
}
} // namespace detail
namespace {
int normalize_index(int n,int nlim) // wraps [-nlim:nlim) into [0:nlim), throw IndexError otherwise
{
if (n<0)
n += nlim; // negative indices work backwards from end
if (n < 0 || n >= nlim)
{
PyErr_SetObject(PyExc_IndexError, Py_None);
throw_error_already_set();
}
return n;
}
}
Py_intptr_t ndarray::shape(int n) const
{
return get_shape()[normalize_index(n,get_nd())];
}
Py_intptr_t ndarray::strides(int n) const
{
return get_strides()[normalize_index(n,get_nd())];
}
ndarray ndarray::view(dtype const & dt) const
{
return ndarray(python::detail::new_reference
(PyObject_CallMethod(this->ptr(), const_cast<char*>("view"), const_cast<char*>("O"), dt.ptr())));
}
ndarray ndarray::astype(dtype const & dt) const
{
return ndarray(python::detail::new_reference
(PyObject_CallMethod(this->ptr(), const_cast<char*>("astype"), const_cast<char*>("O"), dt.ptr())));
}
ndarray ndarray::copy() const
{
return ndarray(python::detail::new_reference
(PyObject_CallMethod(this->ptr(), const_cast<char*>("copy"), const_cast<char*>(""))));
}
dtype ndarray::get_dtype() const
{
return dtype(python::detail::borrowed_reference(get_struct()->descr));
}
python::object ndarray::get_base() const
{
if (get_struct()->base == NULL) return object();
return python::object(python::detail::borrowed_reference(get_struct()->base));
}
void ndarray::set_base(object const & base)
{
Py_XDECREF(get_struct()->base);
if (base.ptr())
{
Py_INCREF(base.ptr());
get_struct()->base = base.ptr();
}
else
{
get_struct()->base = NULL;
}
}
ndarray::bitflag ndarray::get_flags() const
{
return numpy::detail::numpy_to_bitflag(get_struct()->flags);
}
ndarray ndarray::transpose() const
{
return ndarray(python::detail::new_reference
(PyArray_Transpose(reinterpret_cast<PyArrayObject*>(this->ptr()), NULL)));
}
ndarray ndarray::squeeze() const
{
return ndarray(python::detail::new_reference
(PyArray_Squeeze(reinterpret_cast<PyArrayObject*>(this->ptr()))));
}
ndarray ndarray::reshape(python::tuple const & shape) const
{
return ndarray(python::detail::new_reference
(PyArray_Reshape(reinterpret_cast<PyArrayObject*>(this->ptr()), shape.ptr())));
}
python::object ndarray::scalarize() const
{
Py_INCREF(ptr());
return python::object(python::detail::new_reference(PyArray_Return(reinterpret_cast<PyArrayObject*>(ptr()))));
}
ndarray zeros(python::tuple const & shape, dtype const & dt)
{
int nd = len(shape);
boost::scoped_array<Py_intptr_t> dims(new Py_intptr_t[nd]);
for (int n=0; n<nd; ++n) dims[n] = python::extract<Py_intptr_t>(shape[n]);
return ndarray(python::detail::new_reference
(PyArray_Zeros(nd, dims.get(), detail::incref_dtype(dt), 0)));
}
ndarray zeros(int nd, Py_intptr_t const * shape, dtype const & dt)
{
return ndarray(python::detail::new_reference
(PyArray_Zeros(nd, const_cast<Py_intptr_t*>(shape), detail::incref_dtype(dt), 0)));
}
ndarray empty(python::tuple const & shape, dtype const & dt)
{
int nd = len(shape);
boost::scoped_array<Py_intptr_t> dims(new Py_intptr_t[nd]);
for (int n=0; n<nd; ++n) dims[n] = python::extract<Py_intptr_t>(shape[n]);
return ndarray(python::detail::new_reference
(PyArray_Empty(nd, dims.get(), detail::incref_dtype(dt), 0)));
}
ndarray empty(int nd, Py_intptr_t const * shape, dtype const & dt)
{
return ndarray(python::detail::new_reference
(PyArray_Empty(nd, const_cast<Py_intptr_t*>(shape), detail::incref_dtype(dt), 0)));
}
ndarray array(python::object const & obj)
{
return ndarray(python::detail::new_reference
(PyArray_FromAny(obj.ptr(), NULL, 0, 0, NPY_ARRAY_ENSUREARRAY, NULL)));
}
ndarray array(python::object const & obj, dtype const & dt)
{
return ndarray(python::detail::new_reference
(PyArray_FromAny(obj.ptr(), detail::incref_dtype(dt), 0, 0, NPY_ARRAY_ENSUREARRAY, NULL)));
}
ndarray from_object(python::object const & obj, dtype const & dt, int nd_min, int nd_max, ndarray::bitflag flags)
{
int requirements = detail::bitflag_to_numpy(flags);
return ndarray(python::detail::new_reference
(PyArray_FromAny(obj.ptr(),
detail::incref_dtype(dt),
nd_min, nd_max,
requirements,
NULL)));
}
ndarray from_object(python::object const & obj, int nd_min, int nd_max, ndarray::bitflag flags)
{
int requirements = detail::bitflag_to_numpy(flags);
return ndarray(python::detail::new_reference
(PyArray_FromAny(obj.ptr(),
NULL,
nd_min, nd_max,
requirements,
NULL)));
}
}}} // namespace boost::python::numpy