On Mon, Mar 21, 2011 at 11:27 AM, Brent Pedersen wrote:
> my current use-case is to do this 24 times on arrays of about 200K elements.
> file IO is the major bottleneck.
Would using h5py or pytables help? I get about 3 ms for a write-read
cycle for a 200K array. That's much faster than the convo
On Mon, Mar 21, 2011 at 11:57 AM, Keith Goodman wrote:
> On Mon, Mar 21, 2011 at 10:34 AM, Brent Pedersen wrote:
>> On Mon, Mar 21, 2011 at 11:19 AM, Keith Goodman wrote:
>>> On Mon, Mar 21, 2011 at 10:10 AM, Brent Pedersen wrote:
hi, is there a way to take the product along a 1-d array in
On Mon, Mar 21, 2011 at 12:10 PM, Brent Pedersen wrote:
> hi, is there a way to take the product along a 1-d array in a moving
> window? -- similar to convolve, with product in place of sum?
> currently, i'm column_stacking the array with offsets of itself into
> window_size columns and then taki
On Mon, Mar 21, 2011 at 10:34 AM, Brent Pedersen wrote:
> On Mon, Mar 21, 2011 at 11:19 AM, Keith Goodman wrote:
>> On Mon, Mar 21, 2011 at 10:10 AM, Brent Pedersen wrote:
>>> hi, is there a way to take the product along a 1-d array in a moving
>>> window? -- similar to convolve, with product in
On Mon, Mar 21, 2011 at 11:19 AM, Keith Goodman wrote:
> On Mon, Mar 21, 2011 at 10:10 AM, Brent Pedersen wrote:
>> hi, is there a way to take the product along a 1-d array in a moving
>> window? -- similar to convolve, with product in place of sum?
>> currently, i'm column_stacking the array wit
On Mon, Mar 21, 2011 at 10:10 AM, Brent Pedersen wrote:
> hi, is there a way to take the product along a 1-d array in a moving
> window? -- similar to convolve, with product in place of sum?
> currently, i'm column_stacking the array with offsets of itself into
> window_size columns and then takin
hi, is there a way to take the product along a 1-d array in a moving
window? -- similar to convolve, with product in place of sum?
currently, i'm column_stacking the array with offsets of itself into
window_size columns and then taking the product at axis 1.
like::
w = np.column_stack(a[i:-windo
