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switch to row_major default by wolfv · Pull Request #195 · xtensor-stack/xtensor-python · GitHub

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switch to row_major default #195

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22 changes: 20 additions & 2 deletions docs/source/array_tensor.rst
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Expand Up @@ -4,8 +4,8 @@

The full license is in the file LICENSE, distributed with this software.

Arrays and tensors
==================
Arrays, Tensors and NumPy Views
===============================

``xtensor-python`` provides two container types wrapping numpy arrays: ``pyarray`` and ``pytensor``. They are the counterparts
to ``xarray`` and ``xtensor`` containers.
Expand All @@ -25,3 +25,21 @@ Like ``xtensor``, ``pytensor`` has a static stack-allocated shape. This means th
the shape of the ``pytensor`` upon creation. As a consequence, reshapes are not reflected across languages. However, this drawback
is offset by a more effective computation of shape and broadcast.

NumPy views
-----------

If you are trying to call a xtensor function with a NumPy view, you might read the following:
"passing container with wrong strides for static layout". This means, that the *static layout*
of the pytensor does not match the layout of the NumPy array (or view) that you are using to
call the function.

One fix for this is to choose the ``dynamic`` layout for your pyarray or pytensor:

.. code::

void my_function(xt::pyarray<double, xt::layout_type::dynamic> input)

When choosing the dynamic layout, you can pass any NumPy container (with Fortran order, or
an arbitrary view).
Note that iterators on a pyarray with dynamic layout are generally slower!

8 changes: 4 additions & 4 deletions include/xtensor-python/pyarray.hpp
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Expand Up @@ -23,7 +23,7 @@

namespace xt
{
template <class T, layout_type L = layout_type::dynamic>
template <class T, layout_type L = layout_type::row_major>
class pyarray;
}

Expand Down Expand Up @@ -682,7 +682,7 @@ namespace xt

if (!tmp)
{
throw std::runtime_error("NumPy: unable to create ndarray");
throw std::runtime_error("pyarray: unable to create ndarray");
}

this->m_ptr = tmp.release().ptr();
Expand Down Expand Up @@ -771,7 +771,7 @@ namespace xt

if (!tmp)
{
throw std::runtime_error("NumPy: unable to create ndarray");
throw std::runtime_error("pyarray: unable to create ndarray");
}

this->m_ptr = tmp.release().ptr();
Expand All @@ -793,7 +793,7 @@ namespace xt

if (L != layout_type::dynamic && !do_strides_match(m_shape, m_strides, L, 1))
{
throw std::runtime_error("NumPy: passing container with bad strides for layout (is it a view?).");
throw std::runtime_error("pyarray: passing container with wrong strides for static layout. If you are trying to pass a view, set the pytensor layout template parameter to `layout_type::dynamic`, or `layout_type::column_major` for col major.");
}

m_backstrides = backstrides_type(*this);
Expand Down
8 changes: 4 additions & 4 deletions include/xtensor-python/pytensor.hpp
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Expand Up @@ -24,7 +24,7 @@

namespace xt
{
template <class T, std::size_t N, layout_type L = layout_type::dynamic>
template <class T, std::size_t N, layout_type L = layout_type::row_major>
class pytensor;
}

Expand Down Expand Up @@ -431,7 +431,7 @@ namespace xt

if (!tmp)
{
throw std::runtime_error("NumPy: unable to create ndarray");
throw std::runtime_error("pytensor: unable to create ndarray");
}

this->m_ptr = tmp.release().ptr();
Expand All @@ -452,7 +452,7 @@ namespace xt

if (PyArray_NDIM(this->python_array()) != N)
{
throw std::runtime_error("NumPy: ndarray has incorrect number of dimensions");
throw std::runtime_error("pytensor: ndarray has incorrect number of dimensions");
}

std::copy(PyArray_DIMS(this->python_array()), PyArray_DIMS(this->python_array()) + N, m_shape.begin());
Expand All @@ -462,7 +462,7 @@ namespace xt

if (L != layout_type::dynamic && !do_strides_match(m_shape, m_strides, L, 1))
{
throw std::runtime_error("NumPy: passing container with bad strides for layout (is it a view?).");
throw std::runtime_error("pytensor: passing container with wrong strides for static layout. If you are trying to pass a view, set the pytensor layout template parameter to `layout_type::dynamic`, or `layout_type::column_major` for col major.");
}

m_storage = storage_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())),
Expand Down
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