ndarray · TallJimbo · Jun 27, 2016 · Jun 27, 2016 · Jun 27, 2016
diff --git a/.travis.yml b/.travis.yml
@@ -0,0 +1,22 @@
+sudo: required
+dist: trusty
+language: cpp
+
+before_install:
+  - export PATH=$(echo $PATH | tr ':' "\n" | sed '/\/opt\/python/d' | tr "\n" ":" | sed "s|::|:|g")
+  - sudo apt-get -qq update
+  - sudo apt-get install -y libboost-python-dev python-numpy python3-numpy libpython3-dev python-sphinx
+  - sudo ln -s /usr/lib/x86_64-linux-gnu/libboost_python-py34.so /usr/lib/x86_64-linux-gnu/libboost_python3.so
+
+script:
+  - mkdir build
+  - cd build
+  - cmake -DPYTHON_EXECUTABLE=$PYTHON .. && make && make test ARGS="-V"
+  - cd ../libs/numpy/doc
+  - make html
+  - cd ../../../
+  - if [ "$PYTHON" == "/usr/bin/python" ]; then scons; fi
+
+env:
+  - PYTHON=/usr/bin/python
+  - PYTHON=/usr/bin/python3
diff --git a/libs/numpy/doc/tutorial/dtype.rst b/libs/numpy/doc/tutorial/dtype.rst
@@ -5,32 +5,32 @@ Here is a brief tutorial to show how to create ndarrays with built-in python dat

 Like before, first get the necessary headers, setup the namespaces and initialize the Python runtime and numpy module::

-	#include <boost/numpy.hpp>
-	#include <iostream>
+    #include <boost/numpy.hpp>
+    #include <iostream>

-	namespace p = boost::python;
-	namespace np = boost::numpy;
+    namespace p = boost::python;
+    namespace np = boost::numpy;

-	int main(int argc, char **argv)
-	{
-	  Py_Initialize();
-	  np::initialize();
+    int main(int argc, char **argv)
+    {
+      Py_Initialize();
+      np::initialize();

 Next, we create the shape and dtype. We use the get_builtin method to get the numpy dtype corresponding to the builtin C++ dtype 
 Here, we will create a 3x3 array passing a tuple with (3,3) for the size, and double as the data type ::

-	  p::tuple shape = p::make_tuple(3, 3);
-	  np::dtype dtype = np::dtype::get_builtin<double>();
-	  np::ndarray a = np::zeros(shape, dtype);
+      p::tuple shape = p::make_tuple(3, 3);
+      np::dtype dtype = np::dtype::get_builtin<double>();
+      np::ndarray a = np::zeros(shape, dtype);

 Finally, we can print the array using the extract method in the python namespace. 
 Here, we first convert the variable into a string, and then extract it as a C++ character array from the python string using the <char const \* > template ::

-	std::cout << "Original array:\n" << p::extract<char const *>(p::str(a)) << std::endl;
+    std::cout << "Original array:\n" << p::extract<char const *>(p::str(a)) << std::endl;

 We can also print the dtypes of the data members of the ndarray by using the get_dtype method for the ndarray ::

-	std::cout << "Datatype is:\n" << p::extract<char const *>(p::str(a.get_dtype())) << std::endl ;
+    std::cout << "Datatype is:\n" << p::extract<char const *>(p::str(a.get_dtype())) << std::endl ;

 We can also create custom dtypes and build ndarrays with the custom dtypes

@@ -52,4 +52,4 @@ We are now ready to create an ndarray with dimensions specified by \*shape\* and

  np::ndarray new_array = np::zeros(shape,custom_dtype);

-	}
+    }
diff --git a/libs/numpy/doc/tutorial/fromdata.rst b/libs/numpy/doc/tutorial/fromdata.rst
@@ -6,16 +6,16 @@ The from_data method makes this possible.

 Like before, first get the necessary headers, setup the namespaces and initialize the Python runtime and numpy module::

-	#include <boost/numpy.hpp>
-	#include <iostream>
+  #include <boost/numpy.hpp>
+  #include <iostream>

-	namespace p = boost::python;
-	namespace np = boost::numpy;
+  namespace p = boost::python;
+  namespace np = boost::numpy;

-	int main(int argc, char **argv)
-	{
-	  Py_Initialize();
-	  np::initialize();
+  int main(int argc, char **argv)
+  {
+    Py_Initialize();
+    np::initialize();

 Create an array in C++ , and pass the pointer to it to the from_data method to create an ndarray::


diff --git a/libs/numpy/doc/tutorial/ndarray.rst b/libs/numpy/doc/tutorial/ndarray.rst
@@ -8,16 +8,16 @@ This tutorial will introduce you to some of the ways in which you can create nda

 First, as before, initialise the necessary namepaces and runtimes ::

-	#include <boost/numpy.hpp>
-	#include <iostream>
+  #include <boost/numpy.hpp>
+  #include <iostream>

-	namespace p = boost::python;
-	namespace np = boost::numpy;
+  namespace p = boost::python;
+  namespace np = boost::numpy;

-	int main(int argc, char **argv)
-	{
-	  Py_Initialize();
-	  np::initialize();
+  int main(int argc, char **argv)
+  {
+    Py_Initialize();
+    np::initialize();

 Let's now create an ndarray from a simple tuple. We first create a tuple object, and then pass it to the array method, to generate the necessary tuple ::


diff --git a/libs/numpy/doc/tutorial/simple.rst b/libs/numpy/doc/tutorial/simple.rst
@@ -5,37 +5,37 @@ Let's start with a simple tutorial to create and modify arrays.

 Get the necessary headers for numpy components and set up necessary namespaces::

-	#include <boost/numpy.hpp>
-	#include <iostream>
+    #include <boost/numpy.hpp>
+    #include <iostream>

-	namespace p = boost::python;
-	namespace np = boost::numpy;
+    namespace p = boost::python;
+    namespace np = boost::numpy;

 Initialise the Python runtime, and the numpy module. Failure to call these results in segmentation errors::

-	int main(int argc, char **argv)
-	{
-	  Py_Initialize();
-	  np::initialize();
+    int main(int argc, char **argv)
+    {
+      Py_Initialize();
+      np::initialize();


 Zero filled n-dimensional arrays can be created using the shape and data-type of the array as a parameter. Here, the shape is 3x3 and the datatype is the built-in float type::

-	  p::tuple shape = p::make_tuple(3, 3);
-	  np::dtype dtype = np::dtype::get_builtin<float>();
-	  np::ndarray a = np::zeros(shape, dtype);
+      p::tuple shape = p::make_tuple(3, 3);
+      np::dtype dtype = np::dtype::get_builtin<float>();
+      np::ndarray a = np::zeros(shape, dtype);

 You can also create an empty array like this ::

-	np::ndarray b = np::empty(shape,dtype);
-	
+    np::ndarray b = np::empty(shape,dtype);
+    
 Print the original and reshaped array. The array a which is a list is first converted to a string, and each value in the list is extracted using extract< >::

-	  std::cout << "Original array:\n" << p::extract<char const *>(p::str(a)) << std::endl;
+      std::cout << "Original array:\n" << p::extract<char const *>(p::str(a)) << std::endl;

-	  // Reshape the array into a 1D array
-	  a = a.reshape(p::make_tuple(9));
-	  // Print it again.
-	  std::cout << "Reshaped array:\n" << p::extract<char const *>(p::str(a)) << std::endl;
-	}
+      // Reshape the array into a 1D array
+      a = a.reshape(p::make_tuple(9));
+      // Print it again.
+      std::cout << "Reshaped array:\n" << p::extract<char const *>(p::str(a)) << std::endl;
+    }

diff --git a/libs/numpy/test/runCmakeTest.sh.in b/libs/numpy/test/runCmakeTest.sh.in
@@ -1,3 +1,3 @@
 #!/bin/bash
 export PYTHONPATH=@CMAKE_LIBRARY_OUTPUT_DIRECTORY@:${PYTHONPATH}
-python $1
+@PYTHON_EXECUTABLE@ $1