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Prevent potential data loss in numpy's dtype::get_itemsize(). #463

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Prevent potential data loss in numpy's dtype::get_itemsize(). #463

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c641a80
Prevent potential data loss in numpy's dtype::get_itemsize().
JohannesWilde Dec 14, 2024
4a0c096
Use boost::python::ssize_t instead of Py_ssize_t - it is available fo…
JohannesWilde Dec 14, 2024
af8a5f7
Exempt negative itemsize from contiguity and alginment.
JohannesWilde Dec 14, 2024
753b434
Add comments.
JohannesWilde Dec 14, 2024
25a1994
Add constness for unmodified internal parameters.
JohannesWilde Dec 14, 2024
1105d37
Change size type to boost::python::ssize_t - as did numpy with versio…
JohannesWilde Dec 14, 2024
6614188
Use the const-accessor methods when using const_iterators.
JohannesWilde Dec 14, 2024
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Change size type to boost::python::ssize_t - as did numpy with versio… 
…n 2.0 and in accordance with PEP 353.
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@JohannesWilde
JohannesWilde committed Dec 14, 2024
commit 1105d37dcc7fe97272d65eec831a260307777c6e
12 changes: 6 additions & 6 deletions src/numpy/ndarray.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -41,14 +41,14 @@ int bitflag_to_numpy(ndarray::bitflag f)

bool is_c_contiguous(std::vector<Py_intptr_t> const & shape,
std::vector<Py_intptr_t> const & strides,
int const itemsize)
boost::python::ssize_t const itemsize)
{
// An itemsize less than 0 is not useful - default to non-contiguity.
if (0 > itemsize) return false;
// Check the strides (stride[n]) match the accumulated shapes as per C-style,
// i.e. starting from rightmost C-index (itemsize * prod_{i in [n, N)} shape[i]).
std::vector<Py_intptr_t>::const_reverse_iterator j = strides.rbegin();
int total = itemsize;
boost::python::ssize_t total = itemsize;
for (std::vector<Py_intptr_t>::const_reverse_iterator i = shape.rbegin(); i != shape.rend(); ++i, ++j)
{
if (total != *j) return false;
Expand All @@ -59,14 +59,14 @@ bool is_c_contiguous(std::vector<Py_intptr_t> const & shape,

bool is_f_contiguous(std::vector<Py_intptr_t> const & shape,
std::vector<Py_intptr_t> const & strides,
int const itemsize)
boost::python::ssize_t const itemsize)
{
// An itemsize less than 0 is not useful - default to non-contiguity.
if (0 > itemsize) return false;
// Check the strides (stride[n]) match the accumulated shapes as per Fortran-style,
// i.e. starting from leftmost C-index (itemsize * prod_{i in [0, n]} shape[i]).
std::vector<Py_intptr_t>::const_iterator j = strides.begin();
int total = itemsize;
boost::python::ssize_t total = itemsize;
for (std::vector<Py_intptr_t>::const_iterator i = shape.begin(); i != shape.end(); ++i, ++j)
{
if (total != *j) return false;
Expand All @@ -76,7 +76,7 @@ bool is_f_contiguous(std::vector<Py_intptr_t> const & shape,
}

bool is_aligned(std::vector<Py_intptr_t> const & strides,
int const itemsize)
boost::python::ssize_t const itemsize)
{
// An itemsize less than 0 is not useful - default to non-aligned.
if (0 > itemsize) return false;
Expand Down Expand Up @@ -128,7 +128,7 @@ ndarray from_data_impl(void * data,
PyErr_SetString(PyExc_ValueError, "Length of shape and strides arrays do not match.");
python::throw_error_already_set();
}
int const itemsize = dt.get_itemsize();
boost::python::ssize_t const 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;
Expand Down