On Wed, Jul 7, 2010 at 10:13 PM, Christoph Gohlke <cgohlke@uci.edu> wrote:
Dear NumPy developers,
I am trying to solve some scipy.sparse TypeError failures reported in [1] and reduced them to the following example:
import numpy a = numpy.array([[1]])
numpy.dot(a.astype('single'), a.astype('longdouble')) array([[1.0]], dtype=float64)
numpy.dot(a.astype('double'), a.astype('longdouble')) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: array cannot be safely cast to required type
Is this exception expected?
I think not. On some platforms longdouble is the same as double, on others it is extended precision or quad precision. On your platform this looks like a bug, on my platform it would be correct except there is a fallback version of dot that works with extended precision. Is there a mix of compilers here, or is it msvc all the way down. In [5]: a = array([[1]]) In [6]: dot(a.astype('single'), a.astype('longdouble')) Out[6]: array([[1.0]], dtype=float128) Also I noticed this:
numpy.array([1]).astype('longdouble').dtype.num 13 numpy.array([1.0]).astype('longdouble').dtype.num 12
Yeah, that is probably correct in a strange sort of way since the two types are identical under the hood. On ubuntu I get In [1]: array([1]).astype('longdouble').dtype.num Out[1]: 13 In [2]: array([1.]).astype('longdouble').dtype.num Out[2]: 13 Type numbers aren't a good way to determine precision in a platform independent way.
I am using Python 2.6.5 for Windows and numpy 1.4.1 compiled with msvc9, where sizeof(longdouble) == sizeof(double).
Chuck