Re: Re: mx.DateTime 3.2.x and numpy

On Wed, Dec 21, 2011 at 2:18 PM, V <[email protected]> wrote:

> M.-A. Lemburg <mal@...> writes:
> > We'll have to install numpy and check the problem. We may well have
> > to patch mxDateTime to make it work with numpy, if this uses its
> > own coercion mechanism.
> >
>
> Marc-Andre,
>
> I ran into the very same problem the other day. I have also found the root
> of
> the problem. It lies in file mx/DateTime/mxDateTime/mxDateTime.c, function
> mxDateTime_RichCompare, specifically this little bit of code:
>
>    else if (PyFloat_Compatible(right)) {
>                /* DateTime op Number -- compare ticks */
>                double t1 = PyFloat_AsDouble(right);
>                double t0 = mxDateTime_AsTicksWithOffset(self, 0, -1);
>
>                if ((t0 == -1.0 || t1 == -1.0) && PyErr_Occurred())
>                        goto onError;
>
>                cmp = (t0 < t1) ? -1 : (t0 > t1) ? 1 : 0;
>        }
>
> Note here that if the thing on the left side of a comparison is an mx
> DateTime,
> and the thing on the right side of the comparison is a numpy array, this if
> clause will trigger (because numpy.ndarray implements a __float__ method),
> and
> the first thing it will attempt to do is to cast the array to a float.
> Now, if
> this array contains more than one element, precisely the exception that
> Christian observes will occur.
>
>    TypeError: only length-1 arrays can be converted to Python scalars
>
> I suppose it's somewhat of a numpy fault in that perhaps they shouldn't
> implement conversion from array to float, if it doesn't work for arrays
> with
> more than one element, but that behavior has been in numpy for ages,
> whereas I
> believe the PyFloat_Compatible check is a semi-new addition to mx DateTime.
>
> Hope this helps, and hope you guys can find a good resolution to this. For
> now,
> I have to patch my local version of mx to work around this issue.
>
> V
>
>
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For what it is worth, as a mere "lurker" on this list, that is an error in
the numpy implementation, and should be reported.  My search attempts to
find information on a justification for this behavior are so far
unsuccessful.

>>> a = numpy.arange(5)
>>> a
array([0, 1, 2, 3, 4])
>>> float(a)
Traceback (most recent call last):
  File "<interactive input>", line 1, in <module>
TypeError: only length-1 arrays can be converted to Python scalars
>>> a = numpy.arange(1)
>>> a
array([0])
>>> float(a)
0.0
>>> a = numpy.arange(5)
>>> a.astype('float')
array([ 0.,  1.,  2.,  3.,  4.])
>>>


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