Hey Nathaniel,
Fascinating! Thanks for the primer! I didn't know that it would check dtype
of values in the whole array. In that case, I would agree that it would be
bad to infer it magically from just the first value, and this can be left
to the users.
Thanks!
Juan.
On Sat, Dec 12, 2015 at 7:0
On Dec 12, 2015 10:53 AM, "Mathieu Dubois"
wrote:
>
> Le 11/12/2015 11:22, Sturla Molden a écrit :
>>
>> Mathieu Dubois wrote:
>>
>>> The point is precisely that, you can't do memory mapping with Npz files
>>> (while it works with Npy files).
>>
>> The operating system can memory map any file. Bu
> >
> > from itertools import chain
> > def fromiter_awesome_edition(iterable):
> > elem = next(iterable)
> > dtype = whatever_numpy_does_to_infer_dtypes_from_lists(elem)
> > return np.fromiter(chain([elem], iterable), dtype=dtype)
> >
> > I think this would be a huge win for usability.
On Fri, Dec 11, 2015, 18:04 David Cournapeau wrote:
On Fri, Dec 11, 2015 at 4:22 PM, Anne Archibald wrote:
Actually, GCC implements 128-bit floats in software and provides them as
__float128; there are also quad-precision versions of the usual functions.
The Intel compiler provides this as well
> What does "true vectorization" mean anyway?
Calling python functions on python objects in a for loop is not really
vectorized. It's much slower than people intend when they use numpy.
Elliot
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Hi All,
astropy `Time` indeed using two doubles internally, but is very limited in
the operations it allows: essentially only addition/subtraction, and
multiplication with/division by a normal double.
It would be great to have better support within numpy; it is a pity to have
a float128 type that
Le 11/12/2015 11:22, Sturla Molden a écrit :
Mathieu Dubois wrote:
The point is precisely that, you can't do memory mapping with Npz files
(while it works with Npy files).
The operating system can memory map any file. But as npz-files are
compressed, you will need to uncompress the contents i
"Thomas Baruchel" wrote:
> While this is obviously the most relevant answer for many users because
> it will allow them to use Numpy arrays exactly
> as they would have used them with native types, the wrong thing is that
> from some point of view "true" vectorization
> will be lost.
What does "
On Fri, Dec 11, 2015 at 11:32 PM, Juan Nunez-Iglesias
wrote:
> Nathaniel,
>
>> IMO this is better than making np.array(iter) internally call list(iter)
>> or equivalent
>
> Yeah but that's not the only option:
>
> from itertools import chain
> def fromiter_awesome_edition(iterable):
> elem = n
