1 files changed, 71 insertions, 0 deletions
diff --git a/src/boost/libs/python/example/numpy/ndarray.cpp b/src/boost/libs/python/example/numpy/ndarray.cpp
new file mode 100644
index 000000000..d7b57aa73
--- /dev/null
+++ b/src/boost/libs/python/example/numpy/ndarray.cpp
@@ -0,0 +1,71 @@
+// Copyright Ankit Daftery 2011-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+/**
+ *  @brief An example to show how to create ndarrays using arbitrary Python sequences.
+ *
+ *  The Python sequence could be any object whose __array__ method returns an array, or any
+ *  (nested) sequence.  This example also shows how to create arrays using both unit and
+ *  non-unit strides.      
+ */
+
+#include <boost/python/numpy.hpp>
+#include <iostream>
+
+namespace p = boost::python;
+namespace np = boost::python::numpy;
+
+#if _MSC_VER
+using boost::uint8_t;
+#endif
+
+int main(int argc, char **argv)
+{
+  // Initialize the Python runtime.
+  Py_Initialize();
+  // Initialize NumPy
+  np::initialize();
+  // Create an ndarray from a simple tuple
+  p::object tu = p::make_tuple('a','b','c') ;
+  np::ndarray example_tuple = np::array (tu) ; 
+  // and from a list
+  p::list l ;
+  np::ndarray example_list = np::array (l) ; 
+  // Optionally, you can also specify a dtype
+  np::dtype dt = np::dtype::get_builtin<int>();
+  np::ndarray example_list1 = np::array (l,dt);
+  // You can also create an array by supplying data.First,create an integer array
+  int data[] = {1,2,3,4} ;
+  // Create a shape, and strides, needed by the function
+  p::tuple shape = p::make_tuple(4) ;
+  p::tuple stride = p::make_tuple(4) ; 
+  // The function also needs an owner, to keep track of the data array passed. Passing none is dangerous
+  p::object own ;
+  // The from_data function takes the data array, datatype,shape,stride and owner as arguments
+  // and returns an ndarray
+  np::ndarray data_ex = np::from_data(data,dt,shape,stride,own);
+  // Print the ndarray we created
+  std::cout << "Single dimensional array ::" << std::endl << p::extract < char const * > (p::str(data_ex)) << std::endl ; 
+  // Now lets make an 3x2 ndarray from a multi-dimensional array using non-unit strides
+  // First lets create a 3x4 array of 8-bit integers
+  uint8_t mul_data[][4] = {{1,2,3,4},{5,6,7,8},{1,3,5,7}};
+  // Now let's create an array of 3x2 elements, picking the first and third elements from each row
+  // For that, the shape will be 3x2
+  shape = p::make_tuple(3,2) ;
+  // The strides will be 4x2 i.e. 4 bytes to go to the next desired row, and 2 bytes to go to the next desired column
+  stride = p::make_tuple(4,2) ; 
+  // Get the numpy dtype for the built-in 8-bit integer data type
+  np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
+  // First lets create and print out the ndarray as is
+  np::ndarray mul_data_ex = np::from_data(mul_data,dt1, p::make_tuple(3,4),p::make_tuple(4,1),p::object());
+  std::cout << "Original multi dimensional array :: " << std::endl << p::extract < char const * > (p::str(mul_data_ex)) << std::endl ; 
+  // Now create the new ndarray using the shape and strides
+  mul_data_ex = np::from_data(mul_data,dt1, shape,stride,p::object());
+  // Print out the array we created using non-unit strides
+  std::cout << "Selective multidimensional array :: "<<std::endl << p::extract < char const * > (p::str(mul_data_ex)) << std::endl ; 
+
+}
+
+