Cython numpy array indexing

Question

I am trying to speed up some python code with cython, and I'm making use of cython's -a option to see where I can improve things. My understanding is that in the generated html file, the highlighted lines are ones where python functions are called - is that correct?

In the following trivial function, I have declared the numpy array argument arr using the buffer syntax. I thought that this allows indexing operations to take place purely in C without having to call python functions. However, cython -a (version 0.15) highlights the line where I set the value of an element of arr, though not the one where i read one of its elements. Why does this happen? Is there a more efficient way of accessing numpy array elements?

import numpy
cimport numpy

def foo(numpy.ndarray[double, ndim=1] arr not None):
    cdef int i
    cdef double elem
    for i in xrange(10):
      elem = arr[i]          #not highlighted
      arr[i] = 1.0 + elem    #highlighted

EDIT: Also, how does the mode buffer argument interact with numpy? Assuming I haven't changed the order argument of numpy.array from the default, is it always safe to use mode='c'? Does this actually make a difference to performance?

EDIT after delnan's comment: arr[i] += 1 also gets highlighted (that is why I split it up in the first place, to see which part of the operation was causing the issue). If I turn off bounds checking to simplify things (this makes no difference to what gets highlighted), the generated c code is:

  /* "ct.pyx":11
 *   cdef int i
 *   cdef double elem
 *   for i in xrange(10):             # <<<<<<<<<<<<<<
 *     elem = arr[i]
 *     arr[i] = 1.0 + elem
 */
  for (__pyx_t_1 = 0; __pyx_t_1 < 10; __pyx_t_1+=1) {
    __pyx_v_i = __pyx_t_1;

    /* "ct.pyx":12
 *   cdef double elem
 *   for i in xrange(10):
 *     elem = arr[i]             # <<<<<<<<<<<<<<
 *     arr[i] = 1.0 + elem
 */
    __pyx_t_2 = __pyx_v_i;
    __pyx_v_elem = (*__Pyx_BufPtrStrided1d(double *, __pyx_bstruct_arr.buf, __pyx_t_2, __pyx_bstride_0_arr));

    /* "ct.pyx":13
 *   for i in xrange(10):
 *     elem = arr[i]
 *     arr[i] = 1.0 + elem             # <<<<<<<<<<<<<<
 */
    __pyx_t_3 = __pyx_v_i;
    *__Pyx_BufPtrStrided1d(double *, __pyx_bstruct_arr.buf, __pyx_t_3, __pyx_bstride_0_arr) = (1.0 + __pyx_v_elem);
  }

Answer 1

The answer is that the highlighter fools the reader. I compiled your code and the instructions generated under the highlight are those needed to handle the error cases and the return value, they are not related to the array assignment.

Indeed if you change the code to read :

def foo(numpy.ndarray[double, ndim=1] arr not None):
    cdef int i
    cdef double elem
    for i in xrange(10):
      elem = arr[i]
      arr[i] = 1.0 + elem
    return # + add this

The highlight would be on the last line and not more in the assignment.

You can further speed up your code by using the @cython.boundscheck:

import numpy
cimport numpy
cimport cython

@cython.boundscheck(False)
def foo(numpy.ndarray[double, ndim=1] arr not None):
    cdef int i
    cdef double elem
    for i in xrange(10):
      elem = arr[i]
      arr[i] = 1.0 + elem
    return