[SciPy-user] How to "move" data within an array
Wed Jul 4 14:05:28 CDT 2007
Another way to use a list of arrays is to it as a FIFO (First In First
Out): (However, I realize this won't work with your other requirements
such as sum()).
LoA = 
for new_array in new_array_source():
if len(LoA) > 5:
old_array = LoA.pop(0)
Andrew Smart wrote:
> Hi Lorenzo,
> thanks for your 2cents.
> The pointer method isn't practical for my purposes: I want to have the
> ability to access the "historical" data within the engine on various ways,
> e.g. "price average of the last 3 periods", where the array itself stores
> still 5 periods. The pointer method would require to re-calculate the time
> axis and especially to manage the "wrap", like: current time row is 3, so "3
> periods back" would be rows 2, 1 and 5.
> I would like to use the numpy functions as sum(), avg() etc. on the arrays,
> so having single 1d arrays (one row = one array) does not really make sense.
> But thanks for the idea,
> Von: email@example.com
> [mailto:firstname.lastname@example.org] Im Auftrag von lorenzo bolla
> Gesendet: Mittwoch, 4. Juli 2007 11:33
> An: SciPy Users List
> Betreff: Re: [SciPy-user] How to "move" data within an array
> why not using a list of 1D arrays?
> but why do you want to physically move your rows? you can simply use
> an integer as a pointer to the row of the "current time": then you update
> this integer every timestep (+1), taking its "modulo 5" to cycle through the
> my two cents.
> On 7/4/07, Andrew Smart <email@example.com> wrote:
> Hi folks,
> I'm using numpy arrays for storing data which is generated
> within an engine.
> I'm using the topmost dimension as time axis: every row
> represents a full
> set of data created by the engine while one round.
> Say: i have an array for storing prices (e.g. 10 different
> prices are
> generated within one engine round). I'm storing/using the
> last 5 rounds, so
> I get an array with the dimensions (5,10).
> If the engine runs longer than 5 rounds I have to "remove"
> the oldest record
> and move the younger records one position back.
> Since I've a lot of such arrays I would like to use the most
> method avaiable in numpy. On a pure memory-orientated view
> this would be
> just to copy ("move") the memory blocks from the younger 4
> rows one row
> further, thus having the first row for the new data.
> In the C API I see some functions like copyswap() and
> memmove() which
> indicate that such operations are possible at the C API
> level. But I'm not
> sure the correct approach on the Python level.
> Taking slices may be one options - but the new slice will
> then occupy new
> memory, causing memory fragmentation...
> Looping over all data items, all rows is time consuming and
> surely wasting
> Any pointers/ideas?
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