core: add the possibility to share or not the memory between Numpy and Eigen

Author: jcarpent

include/eigenpy/numpy-allocator.hpp

@@ -40,12 +40,19 @@ namespace eigenpy
       typedef typename SimilarMatrixType::Scalar Scalar;
       enum { NPY_ARRAY_MEMORY_CONTIGUOUS = SimilarMatrixType::IsRowMajor ? NPY_ARRAY_CARRAY : NPY_ARRAY_FARRAY };
 
-      PyArrayObject * pyArray = (PyArrayObject*) call_PyArray_New(nd, shape,
-                                                                  NumpyEquivalentType<Scalar>::type_code,
-                                                                  mat.data(),
-                                                                  NPY_ARRAY_MEMORY_CONTIGUOUS | NPY_ARRAY_ALIGNED);
-      
-      return pyArray;
+      if(NumpyType::sharedMemory())
+      {
+        PyArrayObject * pyArray = (PyArrayObject*) call_PyArray_New(nd, shape,
+                                                                    NumpyEquivalentType<Scalar>::type_code,
+                                                                    mat.data(),
+                                                                    NPY_ARRAY_MEMORY_CONTIGUOUS | NPY_ARRAY_ALIGNED);
+        
+        return pyArray;
+      }
+      else
+      {
+        return NumpyAllocator<MatType>::allocate(mat.derived(),nd,shape);
+      }
     }
   };
 
@@ -68,12 +75,19 @@ namespace eigenpy
       typedef typename SimilarMatrixType::Scalar Scalar;
       enum { NPY_ARRAY_MEMORY_CONTIGUOUS_RO = SimilarMatrixType::IsRowMajor ? NPY_ARRAY_CARRAY_RO : NPY_ARRAY_FARRAY_RO };
 
-      PyArrayObject * pyArray = (PyArrayObject*) call_PyArray_New(nd, shape,
-                                                                  NumpyEquivalentType<Scalar>::type_code,
-                                                                  const_cast<SimilarMatrixType &>(mat.derived()).data(),
-                                                                  NPY_ARRAY_MEMORY_CONTIGUOUS_RO | NPY_ARRAY_ALIGNED);
-      
-      return pyArray;
+      if(NumpyType::sharedMemory())
+      {
+        PyArrayObject * pyArray = (PyArrayObject*) call_PyArray_New(nd, shape,
+                                                                    NumpyEquivalentType<Scalar>::type_code,
+                                                                    const_cast<SimilarMatrixType &>(mat.derived()).data(),
+                                                                    NPY_ARRAY_MEMORY_CONTIGUOUS_RO | NPY_ARRAY_ALIGNED);
+                                                                    
+        return pyArray;
+      }
+      else
+      {
+        return NumpyAllocator<MatType>::allocate(mat.derived(),nd,shape);
+      }
     }
   };
 

include/eigenpy/numpy-type.hpp

@@ -96,6 +96,16 @@ namespace eigenpy
         switchToNumpyArray();
     }
 
+    static void sharedMemory(const bool value)
+    {
+      getInstance().shared_memory = value;
+    }
+    
+    static bool sharedMemory()
+    {
+      return getInstance().shared_memory;
+    }
+    
     static void switchToNumpyArray()
     {
       getInstance().CurrentNumpyType = getInstance().NumpyArrayObject;
@@ -162,6 +172,8 @@ namespace eigenpy
 
       CurrentNumpyType = NumpyArrayObject; // default conversion
       np_type = ARRAY_TYPE;
+      
+      shared_memory = true;
     }
 
     bp::object CurrentNumpyType;
@@ -173,6 +185,8 @@ namespace eigenpy
     bp::object NumpyArrayObject; PyTypeObject * NumpyArrayType;
 
     NP_TYPE np_type;
+    
+    bool shared_memory;
   };
 }
 

src/eigenpy.cpp

@@ -45,6 +45,17 @@ namespace eigenpy
     bp::def("switchToNumpyMatrix",&NumpyType::switchToNumpyMatrix,
             "Set the conversion from Eigen::Matrix to numpy.matrix.");
 
+    bp::def("sharedMemory",
+            (void (*)(const bool))NumpyType::sharedMemory,
+            bp::arg("value"),
+            "Share the memory when converting Eigen::Matrix to numpy.array.");
+    
+    bp::def("sharedMemory",
+            (bool (*)())NumpyType::sharedMemory,
+            "Status of the shared memory when converting Eigen::Matrix to numpy.array.\n"
+            "If True, the memory is shared when converting an Eigen::Matrix to a numpy.array.\n"
+            "Otherwise, a deep copy of the Eigen::Matrix is performed");
+    
     bp::def("seed",&seed,bp::arg("seed_value"),
             "Initialize the pseudo-random number generator with the argument seed_value.");