[SciPy-dev] numpy long vs. int

[Damian Eads]

Hi there,

I had some problems porting some internal extension code I wrote recently.
The code worked fine on 32-bit but did not work on 64-bit. When np.int_ is
used as the dtype argument to np.zeros or np.asarray, an array results
that has typecode NPY_INT on 32-bit and NPY_LONG on 64-bit. This
inconsistency is problematic when type checking arrays in C-space prior to
passing them to a C function expecting a specific type, like int. Changing
dtype=np.int_ to dtype='i' seems to consistently result in an array with
typecode NPY_INT on both architectures, which is desired.

I didn't think to write about this until I encountered the very same
problem today when trying to compile the ANN scikit on 64-bit. All the
tests failed because the kd-tree array passed was of type long.

======================================================================
ERROR: test_ann.TestANNWrapper.test_knn_returns_nearest_neighbor(array([[
0.53209373,  0.2149725 ],
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/mirror/ssrc/eads/sci-tools/prefix-x86-64/lib/python2.5/site-packages/nose-0.10.3-py2.5.egg/nose/case.py",
line 182, in runTest
    self.test(*self.arg)
  File
"/mirror/ssrc/eads/sci-tools-source/x86-64/ann/scikits/ann/tests/test_ann.py",
line 49, in checkReturnNN
    nn,nn_distances = tree.knn(pt,1)
  File "scikits/ann/__init__.py", line 113, in knn
    self._knn2(pts, idx, d2, eps)
  File "scikits/ann/ANN.py", line 45, in _knn2
    def _knn2(*args): return _ANN._kdtree__knn2(*args)
TypeError: Array of type 'int' required.  Array of type 'long' given
=================================

Changing line 110 from dtype=np.int_ to dtype='i' fixed the problem. Some
people seem insistent on using a type object (e.g. np.int_ or np.float_)
instead of a string. In fact, when I checked in my hierarchical clustering
code, I noticed someone eventually changed all the dtype's in my code to
use the type objects. I had no qualms with this until now. Are there type
objects that can be passed to dtype to guarantee consistency in
translation to NPY_XXX type codes? We should probably write a caveat in
the Numpy C extensions help document explaining this inconsistency.

Thanks,

Damian