[SciPy-dev] numpy long vs. int
Fri Jul 25 17:36:35 CDT 2008
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.
0.53209373, 0.2149725 ],
Traceback (most recent call last):
line 182, in runTest
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.
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