[SciPy-user] How to "move" data within an array
Wed Jul 4 04:44:59 CDT 2007
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,
[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:
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
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
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
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