A Thursday 23 July 2009 21:17:17 escriguéreu:
[clip]
> > Well, if done properly, I/O in PyTables should not take much more than
> > numpy.memmap (in fact, it can be faster in many occasions). You just
> > need to read/write arrays following the contiguous direction, i.e. the
> > most to the right
On Wed, Jul 29, 2009 at 8:24 AM, Francesc Alted wrote:
> A Thursday 23 July 2009 21:17:17 escriguéreu:
>> The slow part of our code is a single line of Python:
>> `numpy.dot(matrix, vector)`. `matrix` is a tall (thousands by 50)
>> PyTables Array or CArray. `vector` is a row sliced out of it (1 by
A Thursday 23 July 2009 21:17:17 escriguéreu:
> On Wed, Jul 22, 2009 at 5:11 AM, Francesc Alted wrote:
> > A Tuesday 21 July 2009 23:07:28 Kenneth Arnold escrigué:
> >> (Use case: we have a read-only matrix that's an array of vectors.
> >> Given a probe vector, we want to find the top n vectors clo
On Wed, Jul 22, 2009 at 5:11 AM, Francesc Alted wrote:
> A Tuesday 21 July 2009 23:07:28 Kenneth Arnold escrigué:
>> (Use case: we have a read-only matrix that's an array of vectors.
>> Given a probe vector, we want to find the top n vectors closest to it,
>> measured by dot product. numpy's dot fu
Hi Ken,
A Tuesday 21 July 2009 23:07:28 Kenneth Arnold escrigué:
> (Re-raising an issue that was brought up last year: [1])
>
> Since the Enthought webinar on memmap-ing numpy arrays[2] suggested
> PyTables for creating new files (see slide 30 at [3]), I assumed by
> association that PyTables mem-
(Re-raising an issue that was brought up last year: [1])
Since the Enthought webinar on memmap-ing numpy arrays[2] suggested
PyTables for creating new files (see slide 30 at [3]), I assumed by
association that PyTables mem-mapped the data also. I switched an
algorithm that kept data in memory over
