[Numpy-discussion] Quaternion data type
aldcroft at head.cfa.harvard.edu
Sat May 5 21:15:34 EDT 2012
On Sat, May 5, 2012 at 12:55 PM, Charles R Harris
<charlesr.harris at gmail.com> wrote:
> On Sat, May 5, 2012 at 5:27 AM, Tom Aldcroft <aldcroft at head.cfa.harvard.edu>
>> On Fri, May 4, 2012 at 11:44 PM, Ilan Schnell <ischnell at enthought.com>
>> > Hi Chuck,
>> > thanks for the prompt reply. I as curious because because
>> > someone was interested in adding http://pypi.python.org/pypi/Quaternion
>> > to EPD, but Martin and Mark's implementation of quaternions
>> > looks much better.
>> Hi -
>> I'm a co-author of the above mentioned Quaternion package. I agree
>> the numpy_quaternion version would be better, but if there is no
>> expectation that it will move forward I can offer to improve our
>> Quaternion. A few months ago I played around with making it accept
>> arbitrary array inputs (with similar shape of course) to essentially
>> vectorize the transformations. We never got around to putting this in
>> a release because of a perceived lack of interest / priorities... If
>> this would be useful then let me know.
> Would you be interested in carrying Martin's package forward? I'm not
> opposed to having quaternions in numpy/scipy but there needs to be someone
> to push it and deal with problems if they come up. Martin's package
> disappeared in large part because Martin disappeared. I'd also like to hear
> from Mark about other aspects, as there was also a simple rational user type
> proposed that we were looking to put in as an extension 'test' type. IIRC,
> there were some needed fixes to Numpy, some of which were postponed in favor
> of larger changes. User types is one of the things we want ot get fixed up.
It would be great to have a quaternion dtype available in numpy, so I
would be interested in carrying this package if nobody else steps
forward. I don't have any experience with numpy internals, but it
looks like most the heavy lifting is done already.
On a related note the AstroPy project has been discussing a time class
suitable for astronomy (with different conversions, time systems, an
option to use 128-bit precision, etc). We have recently talked about
a numpy dtype analogous to datetime64. This might be an opportunity
to understand a bit the mechanics of making a new dtype.
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