Question

Because of array depth issues in PHP, receiving this array from Python becomes truncated with an ellipsis (“…”). I’d like to process the array in Python before returning back to php.

Clarification: I need to maintain the inner sets [135, 121, 81]. These are R, G, B values and I’m tying to group sets that occur more than once. Values in sets need to maintain [1, 2, 3] sequence, NOT [1,2,3,4,5,6,7,8] as some answers have suggested below.

How would you simplify this 3D numpy.ndarray to a collection of unique RGB triples?

Here is how the array is printed by Python:

[[[135 121  81]
  [135 121  81]
  [135 121  81]
  ..., 
  [135 121  81]
  [135 121  81]
  [135 121  81]]

 [[135 121  81]
  [135 121  81]
  [135 121  81]
  ..., 
  [135 121  81]
  [135 121  81]
  [135 121  81]]

 [[ 67  68  29]
  [135 121  81]
  [ 67  68  29]
  ..., 
  [135 121  81]
  [135 121  81]
  [135 121  81]]

 ..., 
 [[200 170  19]
  [200 170  19]
  [200 170  19]
  ..., 
  [ 67  68  29]
  [ 67  68  29]
  [ 67  68  29]]

 [[200 170  19]
  [200 170  19]
  [200 170  19]
  ..., 
  [116 146  15]
  [116 146  15]
  [116 146  15]]

 [[200 170  19]
  [200 170  19]
  [200 170  19]
  ..., 
  [116 146  15]
  [116 146  15]
  [116 146  15]]]

Here is the code that I have attempted:

def uniquify(arr)
    keys = []

    for c in arr:
        if not c in keys:
            keys[c] = 1
        else:
            keys[c] += 1

    return keys

result = uniquify(items)

Answer 1

Based on the representation of your “array”, it looks like you’re working with a numpy.ndarray. This becomes quite a simple problem if that is the case — You can transform to a 1-D iterable simple by using the .flat attribute. To make it unique, you can just use a set:

set(array.flat)

This will give you a set, but you could easily get a list from it:

list(set(array.flat))

Here’s how it works:

>>> array = np.zeros((10,12,42,53))
>>> list(set(array.flat))
[0.0]

As a side note, there’s also np.unique which will give you the unique elements of your array as well.

>>> array = np.zeros((10,12),dtype=int)
>>> print array
[[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 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 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 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 0]]
>>> np.unique(array)
array([0])
>>> array[0,5] = 1
>>> array[4,10] = 42
>>> np.unique(array)
array([ 0,  1, 42])

---

I think I finally got this one figured out:

from itertools import product

items = set(tuple(a[itr+(slice(None),)]) for itr in product(*[range(x) for x in a.shape[:-1]]))
print items

Seems to work. Phew!

How this works — the pieces that you want to keep as triplets are accessed as:

array[X,Y,:]

So, we just need to loop over all of the combinations of X and Y. That is exactly what itertools.product is good for. We can get the valid X and Y in an arbitrary number of dimensions:

[range(x) for x in array.shape[:-1]]

So we pass that to product:

indices_generator = product(*[range(x) for x in array.shape[:-1]])

Now we have something that will generate the first to indices — We just need to construct a tuple to pass to __getitem__ that numpy will interpret as (X,Y,:) — That’s easy, we’re already getting (X,Y) from indices_generator — We just need to tack on an emtpy slice:

all_items = ( array[idx+(slice(None),)] for idx in indices_generator )

Now we can loop over all_items looking for the unique ones with a set:

unique_items = set(tuple(item) for item in all_items)

Now turn this back into a list, or a numpy array or whatever you want for the purposes of passing it back to PHP.