On Thu, Jun 27, 2013 at 12:00 AM, Josè Luis Mietta < joseluismietta@yahoo.com.ar> wrote:

Hi experts! Im writing a code with a numpy array L, the numpy matrix M and the next

script:

for x in L: for l in srange(N): z= l in L if z is False and M[x,l] != 0: L=np.append(L,l)

here, in the end of the cycle, new elements are incorporated to the array

'L'.

I want these new elements be considered as 'x' index in the cycle. When I execute the script I see that only the 'originals' elements of L are considered as 'x'.

How can i fix it?

There are a couple of things going on here. First, "for x in L:" always iterates over the object initially assigned to the name "L". If that name gets reassigned to a different object during the course of the loop, it won't change the iteration. That's just how Python works. Second, if you can modify the object in-place in the loop, that will affect the iteration, but this is usually a bad idea. It becomes very hard to reason about what is going to happen, and you will usually get it wrong. np.append() cannot modify its array argument in-place. numpy arrays are generally of a fixed size throughout their lifetime for various reasons. That's why you had to reassign the result of np.append() back to the name L. You need to use a Python list or some other extendable object in order to modify the iteration in-place. Generally, np.append() is a sign that you need to use some other data structure. from collections import deque # Convert to a list object that is efficient for appending. # We will accumulate results in this list. L = list(L) # Make a First-In-First-Out queue out of the items. # We will pull work items from this queue. queue = deque(L) while queue: x = queue.popleft() for l in srange(N): if l not in L and M[x,l] != 0: # Add it to the results. L.append(l) # And to the work queue for further processing. queue.append(l) # I guess we need this back as an array again. L = np.array(L) -- Robert Kern