gistfile1.py

import numpy as np
import scipy.ndimage as ndimage

# The array you gave above
data = np.array( 
        [
           [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], 
        ])


# Fill holes to make sure we get nice clusters
filled = ndimage.morphology.binary_fill_holes(data)

# Now separate each group of contiguous ones into a distinct value
# This will be an array of values from 1 - num_objects, with zeros
# outside of any contiguous object
objects, num_objects = ndimage.label(filled)

# Now return a list of slices around each object
#  (This is effectively the tuple that you wanted)
object_slices =  ndimage.find_objects(objects)

# Find the object with the largest area
areas = [np.product([x.stop - x.start for x in slc]) for slc in object_slices]
largest = object_slices[np.argmax(areas)]

print data[largest]