On Mon, Feb 9, 2015 at 8:31 AM, Benjamin Root <ben.root@ou.edu> wrote:

I am trying to write up some code that takes advantage of np.tile() on arbitrary array-like objects. I only want to tile along the first axis. Any other axis, if they exist, should be left alone. I first coerce the object using np.asanyarray(), tile it, and then coerce it back to the original type.

The problem seems to be that some of my array-like objects are being "over-coerced", particularly the list of tuples. I tried doing "np.asanyarray(a, dtype='O')", but that still turns it into a 2-D array.

The default constructors will drill down until they find a scalar or a non-matching shape. So you get an array full of Python ints, or floats, but still 2-D:

>>> a = [tuple(range(j, j+3)) for j in range(5)]

>>> a

[(0, 1, 2), (1, 2, 3), (2, 3, 4), (3, 4, 5), (4, 5, 6)]

>>> np.asarray(a, dtype=object)

array([[0, 1, 2],

       [1, 2, 3],

       [2, 3, 4],

       [3, 4, 5],

       [4, 5, 6]], dtype=object)

If you add a non-matching item, e.g. an empty tuple, then all works fine for your purposes:

>>> a.append(())

>>> np.asarray(a, dtype=object)

array([(0, 1, 2), (1, 2, 3), (2, 3, 4), (3, 4, 5), (4, 5, 6), ()], dtype=object)

But you would then have to discard that item before tiling. The only other way is to first create the object array, then assign your array-like object to it:

>>> a.pop()

()

>>> a

[(0, 1, 2), (1, 2, 3), (2, 3, 4), (3, 4, 5), (4, 5, 6)]

>>> b = np.empty(len(a), object)

>>> b[:] = a

>>> b

array([(0, 1, 2), (1, 2, 3), (2, 3, 4), (3, 4, 5), (4, 5, 6)], dtype=object)

Not sure if this has always worked, or if it breaks down in some corner case, but Wes may not have had to write that function after all! At least not in Cython.

Jaime

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