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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.data()),

const_cast<Py_intptr_t*>(strides.data()),

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