>2010/6/8 Hans Meine :
> On Tuesday 08 June 2010 11:40:59 Scott Sinclair wrote:
>> The savez docstring should probably be clarified to provide this
>> information.
>
> I would prefer to actually offer compression to the user.
In the meantime, I've edited the docstring to reflect the current
behavi
Hi Anne,
thanks for your input, too.
On Tuesday 08 June 2010 12:53:51 Anne Archibald wrote:
> I'm also a little dubious about making compression the default.
> np.savez provides a feature - storing multiple arrays - that is not
> otherwise available. I suspect many users care more about speed th
On 8 June 2010 06:11, Pauli Virtanen wrote:
> ti, 2010-06-08 kello 12:03 +0200, Hans Meine kirjoitti:
>> On Tuesday 08 June 2010 11:40:59 Scott Sinclair wrote:
>> > The savez docstring should probably be clarified to provide this
>> > information.
>>
>> I would prefer to actually offer compression
On Tuesday 08 June 2010 12:11:28 Pauli Virtanen wrote:
> ti, 2010-06-08 kello 12:03 +0200, Hans Meine kirjoitti:
> > I would prefer to actually offer compression to the user. Unfortunately,
> > adding another argument to this function will never be 100% secure, since
> > currently, all kwargs will
ti, 2010-06-08 kello 12:03 +0200, Hans Meine kirjoitti:
> On Tuesday 08 June 2010 11:40:59 Scott Sinclair wrote:
> > The savez docstring should probably be clarified to provide this
> > information.
>
> I would prefer to actually offer compression to the user. Unfortunately,
> adding another argu
On Tuesday 08 June 2010 11:40:59 Scott Sinclair wrote:
> The savez docstring should probably be clarified to provide this
> information.
I would prefer to actually offer compression to the user. Unfortunately,
adding another argument to this function will never be 100% secure, since
currently,
>2010/6/8 Hans Meine :
> I just wondered why numpy.load("foo.npz") was so much faster than loading
> (gzip-compressed) hdf5 file contents, and found that numpy.savez did not
> compress my files at all.
>
> But is that intended? The numpy.savez docstring says "Save several arrays
> into a single, *
Hi,
I just wondered why numpy.load("foo.npz") was so much faster than loading
(gzip-compressed) hdf5 file contents, and found that numpy.savez did not
compress my files at all. So there is currently no point in using numpy.savez
instead of numpy.save when you're not using the multiple-arrays-p
