Question

I’ve this kind of structure:

[
    array([ 0. ,  4.5,  9. ]),
    [
        array([ 100.,  120.,  140.]),
        [
            array([ 1000.,  1100.,  1200.]), 
            array([ 1200.,  1300.,  1400.])
        ],
        array([ 150.,  170.,  190.]),
        [
            array([ 1500.,  1600.,  1700.]), 
            array([ 1700.,  1800.])
        ]
    ]
]

(where arrays are numpy.arrays)

how to write a generator that give me:

(0, 4.5), (100, 120), (1000, 1100)
(0, 4.5), (100, 120), (1100, 1200)
(0, 4.5), (120, 140), (1200, 1300)
(0, 4.5), (120, 140), (1300, 1400)
(4.5, 9), (150, 170), (1500, 1600)
(4.5, 9), (150, 170), (1600, 1700)
(4.5, 9), (170, 190), (1700, 1800)

by now, the only thig I have is:

def loop_bin(bins):
    for i in range(len(bins)-1):
        yield [bins[i], bins[i+1]]

Answer 1

What about:

def foo(m):

  for i in range(0, len(m), 2):

    for j in range(len(m[i])-1):
      current = tuple(m[i][j:(j+2)])
      mm = m[i+1]
      if(len(mm) % 2 != 0 or (len(mm) > 1 and not type(mm[1][0]) is types.ListType)):
        currentl = mm[j]
        for k in range(0, len(currentl)-1):
          yield current, tuple(currentl[k:(k+2)])

      else:
        for res in foo(mm[2*j:2*j+2]):
          # this is for pretty print only
          if type(res) is types.TupleType and len(res)>1 and not type(res[0]) is types.TupleType:
            yield current, res
          else:
            # pretty print again
            c = [current]
            c+= res
            yield tuple(c)

The tuple things are for pretty print, in order to get closer to your example. I was not very sure on the criteria to use for detecting a leaf. Note also that I made my experiments with the following pythonic array:

arr = [
    [ 0. ,  4.5,  9. ],
    [
        [100.,  120.,  140.],
        [
            [ 1000.,  1100.,  1200.], 
            [ 1200.,  1300.,  1400.]
        ],
        [ 150.,  170.,  190.],
        [
            [ 1500.,  1600.,  1700.], 
            [ 1700.,  1800.]
        ]
    ]
]

rather than the numpy array given, but the changes to get things running with numarray should be straightforward.