On Fri, Aug 26, 2011 at 07:04, Derek Homeier
wrote:
> On 25.08.2011, at 8:42PM, Chris.Barker wrote:
>
>> On 8/24/11 9:22 AM, Anthony Scopatz wrote:
>>> You can use Python pickling, if you do *not* have a requirement for:
>>
>> I can't recall why, but it seem pickling of numpy arrays has been
>>
On 8/26/11 5:04 AM, Derek Homeier wrote:
> Hmm, the pure Python version might be, but, I've used cPickle for a long time
> and never noted any stability problems.
well, here is the NEP:
https://github.com/numpy/numpy/blob/master/doc/neps/npy-format.txt
It addresses the why's and hows of the for
On 25.08.2011, at 8:42PM, Chris.Barker wrote:
> On 8/24/11 9:22 AM, Anthony Scopatz wrote:
>>You can use Python pickling, if you do *not* have a requirement for:
>
> I can't recall why, but it seem pickling of numpy arrays has been
> fragile and not very performant.
>
Hmm, the pure Python v
On 25. aug. 2011, at 23.49, David Warde-Farley wrote:
> On 2011-08-25, at 2:42 PM, Chris.Barker wrote:
>
>> On 8/24/11 9:22 AM, Anthony Scopatz wrote:
>>> You can use Python pickling, if you do *not* have a requirement for:
>>
>> I can't recall why, but it seem pickling of numpy arrays has be
On 2011-08-25, at 2:42 PM, Chris.Barker wrote:
> On 8/24/11 9:22 AM, Anthony Scopatz wrote:
>>You can use Python pickling, if you do *not* have a requirement for:
>
> I can't recall why, but it seem pickling of numpy arrays has been
> fragile and not very performant.
>
> I like the npy / np
On 8/24/11 9:22 AM, Anthony Scopatz wrote:
> You can use Python pickling, if you do *not* have a requirement for:
I can't recall why, but it seem pickling of numpy arrays has been
fragile and not very performant.
I like the npy / npz format, built in to numpy, if you don't need:
> - acc
On Sun, Aug 21, 2011 at 7:24 AM, Pauli Virtanen wrote:
> On Sat, 20 Aug 2011 16:18:55 -0700, Chris Withers wrote:
> > I've got a tree of nested dicts that at their leaves end in numpy arrays
> > of identical sizes.
> >
> > What's the easiest way to persist these to disk so that I can pick up
> >
On Sat, 20 Aug 2011 16:18:55 -0700, Chris Withers wrote:
> I've got a tree of nested dicts that at their leaves end in numpy arrays
> of identical sizes.
>
> What's the easiest way to persist these to disk so that I can pick up
> with them where I left off?
Depends on your requirements.
You can
Hi!
On 21. aug. 2011, at 00.18, Chris Withers wrote:
> Hi All,
>
> I've got a tree of nested dicts that at their leaves end in numpy arrays
> of identical sizes.
>
> What's the easiest way to persist these to disk so that I can pick up
> with them where I left off?
Probably giving them names
Hi All,
I've got a tree of nested dicts that at their leaves end in numpy arrays
of identical sizes.
What's the easiest way to persist these to disk so that I can pick up
with them where I left off?
What's the most "correct" way to do so?
I'm using IPython if that makes things easier...
I ha
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