add more dimension checks, new from_shape constructor, const intp cas…

include/xtensor-python/pyarray.hpp

@@ -346,6 +346,9 @@ namespace xt
         explicit pyarray(const shape_type& shape, const strides_type& strides, const_reference value);
         explicit pyarray(const shape_type& shape, const strides_type& strides);
 
+        template <class S = shape_type>
+        static pyarray from_shape(S&& s);
+
         pyarray(const self_type& rhs);
         self_type& operator=(const self_type& rhs);
 
@@ -605,6 +608,18 @@ namespace xt
     {
         init_array(shape, strides);
     }
+
+    /**
+     * Allocates and returns an pyarray with the specified shape.
+     * @param shape the shape of the pyarray
+     */
+    template <class T, layout_type L>
+    template <class S>
+    inline pyarray<T, L> pyarray<T, L>::from_shape(S&& shape)
+    {
+        auto shp = xtl::forward_sequence<shape_type>(shape);
+        return self_type(shp);
+    }
     //@}
 
     /**
@@ -726,6 +741,12 @@ namespace xt
                                    static_cast<size_type>(PyArray_NDIM(this->python_array())));
         m_strides = inner_strides_type(reinterpret_cast<size_type*>(PyArray_STRIDES(this->python_array())),
                                        static_cast<size_type>(PyArray_NDIM(this->python_array())));
+
+        if (L != layout_type::dynamic && !do_strides_match(m_shape, m_strides, L))
+        {
+            throw std::runtime_error("NumPy: passing container with bad strides for layout (is it a view?).");
+        }
+
         m_backstrides = backstrides_type(*this);
         m_storage = storage_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())),
                                  this->get_min_stride() * static_cast<size_type>(PyArray_SIZE(this->python_array())));

include/xtensor-python/pycontainer.hpp

@@ -226,8 +226,7 @@ namespace xt
         bool check_array(const pybind11::handle& src)
         {
             using is_arithmetic_type = std::integral_constant<bool, bool(pybind11::detail::satisfies_any_of<T, std::is_arithmetic, xtl::is_complex>::value)>;
-            return PyArray_Check(src.ptr()) &&
-                   check_array_type<T>(src, is_arithmetic_type{});
+            return PyArray_Check(src.ptr()) && check_array_type<T>(src, is_arithmetic_type{});
         }
     }
 
@@ -277,9 +276,7 @@ namespace xt
     template <class D>
     inline bool pycontainer<D>::check_(pybind11::handle h)
     {
-        auto dtype = pybind11::detail::npy_format_descriptor<value_type>::dtype();
-        return PyArray_Check(h.ptr()) &&
-            PyArray_EquivTypes_(PyArray_TYPE(reinterpret_cast<PyArrayObject*>(h.ptr())), dtype.ptr());
+        return detail::check_array<typename D::value_type>(h);
     }
 
     template <class D>
@@ -321,6 +318,30 @@ namespace xt
         return *static_cast<const derived_type*>(this);
     }
 
+    namespace detail
+    {
+        template <class S>
+        struct check_dims
+        {
+            static bool run(std::size_t)
+            {
+                return true;
+            }
+        };
+
+        template <class T, std::size_t N>
+        struct check_dims<std::array<T, N>>
+        {
+            static bool run(std::size_t new_dim)
+            {
+                if(new_dim != N)
+                {
+                    throw std::runtime_error("Dims not matching.");
+                }
+                return new_dim == N;
+            }
+        };
+    }
 
     /**
      * resizes the container.
@@ -359,6 +380,7 @@ namespace xt
     template <class S>
     inline void pycontainer<D>::resize(const S& shape, const strides_type& strides)
     {
+        detail::check_dims<shape_type>::run(shape.size());
         derived_type tmp(xtl::forward_sequence<shape_type>(shape), strides);
         *static_cast<derived_type*>(this) = std::move(tmp);
     }
@@ -369,9 +391,9 @@ namespace xt
     {
         if (compute_size(shape) != this->size())
         {
-            throw std::runtime_error("Cannot reshape with incorrect number of elements.");
+            throw std::runtime_error("Cannot reshape with incorrect number of elements (" + std::to_string(this->size()) + " vs " + std::to_string(compute_size(shape)) + ")");
         }
-
+        detail::check_dims<shape_type>::run(shape.size());
         layout = default_assignable_layout(layout);
 
         NPY_ORDER npy_layout;
@@ -388,7 +410,8 @@ namespace xt
             throw std::runtime_error("Cannot reshape with unknown layout_type.");
         }
 
-        PyArray_Dims dims = {reinterpret_cast<npy_intp*>(shape.data()), static_cast<int>(shape.size())};
+        using shape_ptr = typename std::decay_t<S>::pointer;
+        PyArray_Dims dims = {reinterpret_cast<npy_intp*>(const_cast<shape_ptr>(shape.data())), static_cast<int>(shape.size())};
         auto new_ptr = PyArray_Newshape((PyArrayObject*) this->ptr(), &dims, npy_layout);
         auto old_ptr = this->ptr();
         this->ptr() = new_ptr;

include/xtensor-python/pytensor.hpp

@@ -163,6 +163,9 @@ namespace xt
         explicit pytensor(const shape_type& shape, const strides_type& strides, const_reference value);
         explicit pytensor(const shape_type& shape, const strides_type& strides);
 
+        template <class S = shape_type>
+        static pytensor from_shape(S&& shape);
+
         pytensor(const self_type& rhs);
         self_type& operator=(const self_type& rhs);
 
@@ -315,6 +318,19 @@ namespace xt
     {
         init_tensor(shape, strides);
     }
+
+    /**
+     * Allocates and returns an pytensor with the specified shape.
+     * @param shape the shape of the pytensor
+     */
+    template <class T, std::size_t N, layout_type L>
+    template <class S>
+    inline pytensor<T, N, L> pytensor<T, N, L>::from_shape(S&& shape)
+    {
+        detail::check_dims<shape_type>::run(shape.size());
+        auto shp = xtl::forward_sequence<shape_type>(shape);
+        return self_type(shp);
+    }
     //@}
 
     /**
@@ -429,6 +445,12 @@ namespace xt
         std::transform(PyArray_STRIDES(this->python_array()), PyArray_STRIDES(this->python_array()) + N, m_strides.begin(),
                        [](auto v) { return v / sizeof(T); });
         adapt_strides(m_shape, m_strides, m_backstrides);
+
+        if (L != layout_type::dynamic && !do_strides_match(m_shape, m_strides, L))
+        {
+            throw std::runtime_error("NumPy: passing container with bad strides for layout (is it a view?).");
+        }
+
         m_storage = storage_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())),
                                  this->get_min_stride() * static_cast<size_type>(PyArray_SIZE(this->python_array())));
     }

test/test_pyarray.cpp

@@ -52,6 +52,15 @@ namespace xt
         }
     }
 
+    TEST(pyarray, from_shape)
+    {
+        auto arr = pyarray<double>::from_shape({5, 2, 6});
+        auto exp_shape = std::vector<std::size_t>{5, 2, 6};
+        EXPECT_TRUE(std::equal(arr.shape().begin(), arr.shape().end(), exp_shape.begin()));
+        EXPECT_EQ(arr.shape().size(), 3);
+        EXPECT_EQ(arr.size(), 5 * 2 * 6);
+    }
+
     TEST(pyarray, strided_constructor)
     {
         central_major_result<> cmr;

test/test_pytensor.cpp

@@ -51,6 +51,18 @@ namespace xt
         }
     }
 
+    TEST(pytensor, from_shape)
+    {
+        auto arr = pytensor<double, 3>::from_shape({5, 2, 6});
+        auto exp_shape = std::vector<std::size_t>{5, 2, 6};
+        EXPECT_TRUE(std::equal(arr.shape().begin(), arr.shape().end(), exp_shape.begin()));
+        EXPECT_EQ(arr.shape().size(), 3);
+        EXPECT_EQ(arr.size(), 5 * 2 * 6);
+        using pyt3 = pytensor<double, 3>;
+        std::vector<std::size_t> shp = std::vector<std::size_t>{5, 2};
+        EXPECT_THROW(pyt3::from_shape(shp), std::runtime_error);
+    }
+
     TEST(pytensor, strided_constructor)
     {
         central_major_result<container_type> cmr;
@@ -211,8 +223,12 @@ namespace xt
     {
         pytensor<int, 2> a = {{1,2,3}, {4,5,6}};
         auto ptr = a.data();
+        a.reshape(a.shape()); // compilation check
         a.reshape({1, 6});
         EXPECT_EQ(ptr, a.data());
-        EXPECT_THROW(a.reshape({6}), std::runtime_error);
+        EXPECT_THROW(a.reshape(std::vector<std::size_t>{6}), std::runtime_error);
+        // note this throws because std array has only 1 element initialized
+        // and the second element is `0`.
+        EXPECT_THROW(a.reshape({6, 5}), std::runtime_error);
     }
 }

test_python/main.cpp

@@ -12,6 +12,7 @@
 #include "xtensor/xarray.hpp"
 #define FORCE_IMPORT_ARRAY
 #include "xtensor-python/pyarray.hpp"
+#include "xtensor-python/pytensor.hpp"
 #include "xtensor-python/pyvectorize.hpp"
 
 namespace py = pybind11;
@@ -154,6 +155,22 @@ void char_array(xt::pyarray<char[20]>& carr)
     carr(2)[3] = '\0';
 }
 
+void row_major_tensor(xt::pytensor<double, 3, xt::layout_type::row_major>& arg)
+{
+    if (!std::is_same<decltype(arg.begin()), double*>::value)
+    {
+        throw std::runtime_error("TEST FAILED");
+    }
+}
+
+void col_major_array(xt::pyarray<double, xt::layout_type::column_major>& arg)
+{
+    if (!std::is_same<decltype(arg.template begin<xt::layout_type::column_major>()), double*>::value)
+    {
+        throw std::runtime_error("TEST FAILED");
+    }
+}
+
 PYBIND11_MODULE(xtensor_python_test, m)
 {
     xt::import_numpy();
@@ -197,4 +214,7 @@ PYBIND11_MODULE(xtensor_python_test, m)
     m.def("dtype_to_python", dtype_to_python);
     m.def("dtype_from_python", dtype_from_python);
     m.def("char_array", char_array);
+
+    m.def("col_major_array", col_major_array);
+    m.def("row_major_tensor", row_major_tensor);
 }

test_python/setup.py

@@ -46,6 +46,7 @@ def __str__(self):
         ['main.cpp'],
         include_dirs=[
             # Path to pybind11 headers
+            '../include/',
             get_pybind_include(),
             get_pybind_include(user=True),
             # Path to numpy headers

test_python/test_pyarray.py

@@ -125,3 +125,24 @@ def test_char_array(self):
         self.assertEqual(var[0], b'hello')
         self.assertEqual(var[1], b'from')
         self.assertEqual(var[2], b'c++')
+
+    def test_col_row_major(self):
+        var = np.arange(50, dtype=float).reshape(2, 5, 5)
+
+        with self.assertRaises(RuntimeError):
+            xt.col_major_array(var)
+
+        with self.assertRaises(RuntimeError):
+            xt.row_major_tensor(var.T)
+
+        with self.assertRaises(RuntimeError):
+            xt.row_major_tensor(var[:, ::2, ::2])
+
+        with self.assertRaises(RuntimeError):
+            # raise for wrong dimension
+            xt.row_major_tensor(var[0, 0, :])
+
+        xt.row_major_tensor(var)
+        varF = np.arange(50, dtype=float).reshape(2, 5, 5, order='F')
+        xt.col_major_array(varF)
+        xt.col_major_array(varF[:, :, 0]) # still col major!