[egenix-users] Re: mx.DateTime 3.2.x and numpy

[M.-A. Lemburg]

V wrote:
> M.-A. Lemburg <mal at ...> 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.

Not really, but it's possible that the new per operation
coercion code does things in a different order.

Do you get the same exception when swapping the two arguments ? ...

 t = delta_t + t0

instead of

 t = t0 + delta_t

> 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.

Thank you for the helpful insight.

I guess we will either have to add a special case for numpy arrays
or fallback to try other methods in case conversion to a float
fails.

-- 
Marc-Andre Lemburg
eGenix.com

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