fix resize for 0.15

Author: wolfv

.appveyor.yml

@@ -23,7 +23,7 @@ install:
   - conda update -q conda
   - conda info -a
   - conda install gtest cmake -c conda-forge
-  - conda install xtensor==0.14.0 pytest numpy pybind11==2.2.1 -c conda-forge
+  - conda install xtensor==0.15.0 pytest numpy pybind11==2.2.1 -c conda-forge
   - "set PYTHONHOME=%MINICONDA%"
   - cmake -G "NMake Makefiles" -D CMAKE_INSTALL_PREFIX=%MINICONDA%\\Library -D BUILD_TESTS=ON -D PYTHON_EXECUTABLE=%MINICONDA%\\python.exe .
   - nmake test_xtensor_python

.travis.yml

@@ -95,7 +95,7 @@ install:
     - conda update -q conda
     # Useful for debugging any issues with conda
     - conda info -a
-    - conda install xtensor==0.14.0 pytest numpy pybind11==2.2.1 -c conda-forge
+    - conda install xtensor==0.15.0 pytest numpy pybind11==2.2.1 -c conda-forge
     - conda install cmake gtest -c conda-forge
     - cmake -D BUILD_TESTS=ON -D CMAKE_INSTALL_PREFIX=$HOME/miniconda .
     - make -j2 test_xtensor_python

README.md

@@ -187,7 +187,7 @@ from the `docs` subdirectory.
 
 | `xtensor-python` | `xtensor` |  `pybind11`      |
 |------------------|-----------|------------------|
-| master           |  ^0.14.0  | ~2.1.0 or ~2.2.1 |
+| master           |  ^0.15.0  | ~2.1.0 or ~2.2.1 |
 | 0.16.x           |  ^0.14.0  | ~2.1.0 or ~2.2.1 |
 | 0.15.x           |  ^0.13.1  | ~2.1.0 or ~2.2.1 |
 | 0.14.x           |  ^0.12.0  | ~2.1.0 or ~2.2.1 |

include/xtensor-python/pyarray.hpp

@@ -486,47 +486,47 @@ namespace xt
     inline pyarray<T>::pyarray(const value_type& t)
         : base_type()
     {
-        base_type::reshape(xt::shape<shape_type>(t), layout_type::row_major);
+        base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
         nested_copy(m_data.begin(), t);
     }
 
     template <class T>
     inline pyarray<T>::pyarray(nested_initializer_list_t<T, 1> t)
         : base_type()
     {
-        base_type::reshape(xt::shape<shape_type>(t), layout_type::row_major);
+        base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
         nested_copy(m_data.begin(), t);
     }
 
     template <class T>
     inline pyarray<T>::pyarray(nested_initializer_list_t<T, 2> t)
         : base_type()
     {
-        base_type::reshape(xt::shape<shape_type>(t), layout_type::row_major);
+        base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
         nested_copy(m_data.begin(), t);
     }
 
     template <class T>
     inline pyarray<T>::pyarray(nested_initializer_list_t<T, 3> t)
         : base_type()
     {
-        base_type::reshape(xt::shape<shape_type>(t), layout_type::row_major);
+        base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
         nested_copy(m_data.begin(), t);
     }
 
     template <class T>
     inline pyarray<T>::pyarray(nested_initializer_list_t<T, 4> t)
         : base_type()
     {
-        base_type::reshape(xt::shape<shape_type>(t), layout_type::row_major);
+        base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
         nested_copy(m_data.begin(), t);
     }
 
     template <class T>
     inline pyarray<T>::pyarray(nested_initializer_list_t<T, 5> t)
         : base_type()
     {
-        base_type::reshape(xt::shape<shape_type>(t), layout_type::row_major);
+        base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
         nested_copy(m_data.begin(), t);
     }
 

include/xtensor-python/pycontainer.hpp

@@ -85,11 +85,14 @@ namespace xt
         static constexpr bool contiguous_layout = false;
 
         template <class S = shape_type>
-        void reshape(const S& shape);
+        void resize(const S& shape);
         template <class S = shape_type>
-        void reshape(const S& shape, layout_type l);
+        void resize(const S& shape, layout_type l);
         template <class S = shape_type>
-        void reshape(const S& shape, const strides_type& strides);
+        void resize(const S& shape, const strides_type& strides);
+
+        template <class S = shape_type>
+        void reshape(S&& shape, layout_type layout = base_type::static_layout);
 
         layout_type layout() const;
 
@@ -258,46 +261,79 @@ namespace xt
     }
 
     /**
-     * Reshapes the container.
+     * resizes the container.
      * @param shape the new shape
      */
     template <class D>
     template <class S>
-    inline void pycontainer<D>::reshape(const S& shape)
+    inline void pycontainer<D>::resize(const S& shape)
     {
         if (shape.size() != this->dimension() || !std::equal(std::begin(shape), std::end(shape), std::begin(this->shape())))
         {
-            reshape(shape, layout_type::row_major);
+            resize(shape, layout_type::row_major);
         }
     }
 
     /**
-     * Reshapes the container.
+     * resizes the container.
      * @param shape the new shape
      * @param l the new layout
      */
     template <class D>
     template <class S>
-    inline void pycontainer<D>::reshape(const S& shape, layout_type l)
+    inline void pycontainer<D>::resize(const S& shape, layout_type l)
     {
         strides_type strides = xtl::make_sequence<strides_type>(shape.size(), size_type(1));
         compute_strides(shape, l, strides);
-        reshape(shape, strides);
+        resize(shape, strides);
     }
 
     /**
-     * Reshapes the container.
+     * resizes the container.
      * @param shape the new shape
      * @param strides the new strides
      */
     template <class D>
     template <class S>
-    inline void pycontainer<D>::reshape(const S& shape, const strides_type& strides)
+    inline void pycontainer<D>::resize(const S& shape, const strides_type& strides)
     {
         derived_type tmp(xtl::forward_sequence<shape_type>(shape), strides);
         *static_cast<derived_type*>(this) = std::move(tmp);
     }
 
+    template <class D>
+    template <class S>
+    inline void pycontainer<D>::reshape(S&& shape, layout_type layout)
+    {
+        if (compute_size(shape) != this->size())
+        {
+            throw std::runtime_error("Cannot reshape with incorrect number of elements.");
+        }
+
+        if (layout == layout_type::dynamic || layout == layout_type::any)
+        {
+            layout = DEFAULT_LAYOUT;
+        }
+
+        NPY_ORDER npy_layout;
+        if (layout == layout_type::row_major)
+        {
+            npy_layout = NPY_CORDER;
+        }
+        else if (layout == layout_type::column_major)
+        {
+            npy_layout = NPY_FORTRANORDER;
+        }
+        else
+        {
+            throw std::runtime_error("Cannot reshape with unknown layout_type.");
+        }
+
+        std::vector<npy_intp> int_shape(shape.begin(), shape.end());
+        PyArray_Dims dims({int_shape.data(), static_cast<int>(shape.size())});
+        PyArray_Newshape((PyArrayObject*) this->ptr(), &dims, npy_layout);
+    }
+
     /**
      * Return the layout_type of the container
      * @return layout_type of the container

include/xtensor-python/pytensor.hpp

@@ -237,7 +237,7 @@ namespace xt
     inline pytensor<T, N>::pytensor(nested_initializer_list_t<T, N> t)
         : base_type()
     {
-        base_type::reshape(xt::shape<shape_type>(t), layout_type::row_major);
+        base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
         nested_copy(m_data.begin(), t);
     }