Hi all,
I'm trying to understand why viewing an array as bytes before clearing makes
the whole operation faster.
I imagine there is some kind of special treatment for byte arrays but I've no
clue.
# Native float
Z_float = np.ones(100, float)
Z_int = np.ones(100, int)
%timeit Z_fl
On Mo, 2016-12-26 at 10:34 +0100, Nicolas P. Rougier wrote:
> Hi all,
>
>
> I'm trying to understand why viewing an array as bytes before
> clearing makes the whole operation faster.
> I imagine there is some kind of special treatment for byte arrays but
> I've no clue.
>
Sure, if its a 1-byte
Thanks for the explanation Sebastian, makes sense.
Nicolas
> On 26 Dec 2016, at 11:48, Sebastian Berg wrote:
>
> On Mo, 2016-12-26 at 10:34 +0100, Nicolas P. Rougier wrote:
>> Hi all,
>>
>>
>> I'm trying to understand why viewing an array as bytes before
>> clearing makes the whole operatio
Might be os-specific, too. Some virtual memory management systems might
special case the zeroing out of memory. Try doing the same thing with a
different value than zero.
On Dec 26, 2016 6:15 AM, "Nicolas P. Rougier"
wrote:
Thanks for the explanation Sebastian, makes sense.
Nicolas
> On 26 D
Dear fellows,
I'm struggling with a single script to build numpy from source in a virtual
env. I want the same script to be able to be run with a normal env.
So if ran from a normal env it should affect all users.
If ran within a virtual env the installation should be constrained to that
env.
I
On Tue, Dec 27, 2016 at 9:43 AM, Felipe Vieira wrote:
> Dear fellows,
>
> I'm struggling with a single script to build numpy from source in a
> virtual env. I want the same script to be able to be run with a normal env.
>
> So if ran from a normal env it should affect all users.
> If ran within a
