On 18 Apr 2013 01:29, "Chris Barker - NOAA Federal"
wrote:
> This has been annoying, particular as rank-zero scalars are kind of a
pain.
BTW, while we're on the topic, can you elaborate on this? I tend to think
scalars (as opposed to 0d ndarrays) are kind of a pain, so I'm curious if
you have spe
On 2013-04-19 01:02:59 +, Benjamin Root said:
>
>
>
> On Thu, Apr 18, 2013 at 7:31 PM, K.-Michael Aye
> wrote:
> I don't understand why sometimes a direct assignment of a new dtype is
> possible (but messes up the values), and why at other times a seemingly
> harmless upcast (in my potent
On Thu, Apr 18, 2013 at 7:31 PM, K.-Michael Aye wrote:
> I don't understand why sometimes a direct assignment of a new dtype is
> possible (but messes up the values), and why at other times a seemingly
> harmless upcast (in my potentially ignorant point of view) is not
> possible.
> So, maybe a di
I don't understand why sometimes a direct assignment of a new dtype is
possible (but messes up the values), and why at other times a seemingly
harmless upcast (in my potentially ignorant point of view) is not
possible.
So, maybe a direct assignment of a new dtype is actually never a good
idea?
Hey,
so I ignored trying to redo MapIter (likely it is lobotomized at this
time though). But actually got a working new index parsing (still needs
cleanup, etc.). Also some of the fast paths are not yet put back. For
most pure integer indices it got a bit slower, if it actually gets too
much one
On Thu, Apr 18, 2013 at 9:20 PM, Chris Barker - NOAA Federal
wrote:
> On Thu, Apr 18, 2013 at 8:31 AM, Chris Barker - NOAA Federal
> wrote:
>
>> Fair enough -- so a missing feature, not bug -- I'll need to look at
>> the docs and see if that can be clarified -
>
> All I've found is the docstring
An update--- I submitted a PR if anyone is interested:
https://github.com/numpy/numpy/pull/3262
Secondly, it was pointed out to my by Stefan van der Walt that one could
use np.rollaxis() to reorder an array such that the default iterator
behavior would yield the same slices of the array:
a = np.o
On Thu, Apr 18, 2013 at 8:31 AM, Chris Barker - NOAA Federal
wrote:
> Fair enough -- so a missing feature, not bug -- I'll need to look at
> the docs and see if that can be clarified -
All I've found is the docstring docs (which also show up in the Sphinx
docs). I suggest some slight modificatio
On Wed, Apr 17, 2013 at 11:27 PM, Joris Van den Bossche
wrote:
>> Anyone tested this on Windows?
>
> On Windows 7, numpy 1.7.0 (Anaconda 1.4.0 64 bit), I don't even get a wrong
> answer, but an error:
>
> In [3]: np.datetime64('1969-12-31 00')
> Out[3]: numpy.datetime64('1969-12-31T00:00Z','h')
>
On Wed, Apr 17, 2013 at 6:05 PM, Benjamin Root wrote:
> Aren't we on standard time at Jan 1st? So, at that date, you would have
> been -8.
yes, of course, pardon me for being an idiot.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R
Good morning Numpy list!
A colleague of mine at Brookhaven National Laboratory (Long Island, NY) is
hiring a "Senior Applications Analyst". One of the desired languages is Python,
and as a strong collaborator with this group I can attest anyone with good
python skills will do well there…
Here is
On Thu, Apr 18, 2013 at 4:04 AM, Robert Kern wrote:
> np.save() and company (and the NPY format itself) are for arrays, not
> for scalars. np.save() uses an np.asanyarray() to coerce its input
> which is why your scalar gets converted to a rank-zero array.
Fair enough -- so a missing feature, no
On Thu, Apr 18, 2013 at 2:27 AM, Joris Van den Bossche <
jorisvandenboss...@gmail.com> wrote:
> ANyone tested this on Windows?
>>
>
>
> On Windows 7, numpy 1.7.0 (Anaconda 1.4.0 64 bit), I don't even get a
> wrong answer, but an error:
>
> In [3]: np.datetime64('1969-12-31 00')
> Out[3]: numpy.dat
On Thu, Apr 18, 2013 at 5:58 AM, Chris Barker - NOAA Federal
wrote:
> Folks,
>
> I've discovered somethign intertesting (bug?) with numpy scalars ans
> savz. If I save a numpy scalar, then reload it, ot comes back as
> rank-0 array -- similar, but not the same thing:
>
> In [144]: single_value, ty
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