Hi (sorry if you receive this twice, but I did not see the first post appear) I have a nagging problem which I think could be solved nicely with numpy indexing but I cannot find the solution, except by invoking a stupid loop. I would like to do this with some numpy item. Problem ======== I have a 2D array which is let's say 1000 x 100. This represents 100 1D spectra, each one containing 1000 datapoints. For example: startarray = random((1000,100)) Assuming I have a list of indices, for example: ind = [1,2,5,6,1,2] and a corresponding list of integer shifts let's say between -100 and 100: shift = [10,20,34,-10,22,-20] I would like to do the following sum result = stararray[100+shift[0]:-100+shift[0],ind[0]] + stararray[100+shift[1]:-100+shift[0],ind[0]] + ... Basically: I would like to sum some extracted 1D line after they have been shifted by some amount. Note that I would like to be able to use each line several times (with different shifts). At the moment, I am using "take" to extract from this array some of these spectra, so let's start from scratch here: import numpy as num startarray = random((1000,100)) take_sample = [1,2,5,6,1,2] temp = num.take(startarray,take_sample,axis=1) # and to do the sum after shifting I need a loop shift = [10,20,34,-10,22,-20] result = num.zeros(900) # shorter than initial because of the shift for i in range(len(shift)) : result += temp[100+shift[i]:-100+shift[1]] Is there a way to do this without invoking a loop? Is there also a FAST solution which makes the shifts at the same time than the "take" (that would then prevent the use of the "temp" array)? Of course the arrays I am using are much BIGGER than these, so I really need an efficient way to do all this. thanks! Eric -- -------------------------------------------------------------------------- Ce message a été envoyé depuis le webmail IMP (Internet Messaging Program)
On Tue, Dec 29, 2009 at 7:58 AM, Eric Emsellem <emsellem@obs.univ-lyon1.fr> wrote:
Hi (sorry if you receive this twice, but I did not see the first post appear)
I have a nagging problem which I think could be solved nicely with numpy indexing but I cannot find the solution, except by invoking a stupid loop. I would like to do this with some numpy item.
Problem ======== I have a 2D array which is let's say 1000 x 100. This represents 100 1D spectra, each one containing 1000 datapoints. For example:
startarray = random((1000,100))
Assuming I have a list of indices, for example:
ind = [1,2,5,6,1,2]
and a corresponding list of integer shifts let's say between -100 and 100:
shift = [10,20,34,-10,22,-20]
I would like to do the following sum
result = stararray[100+shift[0]:-100+shift[0],ind[0]] + stararray[100+shift[1]:-100+shift[0],ind[0]] + ...
Basically: I would like to sum some extracted 1D line after they have been shifted by some amount. Note that I would like to be able to use each line several times (with different shifts).
At the moment, I am using "take" to extract from this array some of these spectra, so let's start from scratch here:
import numpy as num startarray = random((1000,100)) take_sample = [1,2,5,6,1,2] temp = num.take(startarray,take_sample,axis=1)
Would it help to make temp a 1000x4 array instead of 1000x6? Could you do that by changing take_sample to [1,2,5,6] and multiplying columns 1 and 2 by a factor of 2? That would slow down the construction of temp but speed up the addition (and slicing?) in the loop below.
# and to do the sum after shifting I need a loop
shift = [10,20,34,-10,22,-20] result = num.zeros(900) # shorter than initial because of the shift for i in range(len(shift)) : result += temp[100+shift[i]:-100+shift[1]]
This looks fast to me. The slicing doesn't make a copy nor does the addition. I've read that cython does fast indexing but I don't know if that applies to slicing as well. I assume that shift[1] is a typo and should be shift[i].
Is there a way to do this without invoking a loop? Is there also a FAST solution which makes the shifts at the same time than the "take" (that would then prevent the use of the "temp" array)?
Of course the arrays I am using are much BIGGER than these, so I really need an efficient way to do all this.
thanks!
Eric
--
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participants (2)
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Eric Emsellem
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Keith Goodman