Slicing without a priori knowledge of dimension
Dear list. I've got a feeling that what I'm trying to do *should* be easy but at the moment I can't find a non-brute-force method. I'm working with quite a high-rank matrix; 7 dimensions filled with chi**2 values. It's form is something like this: chi2 = numpy.ones((3,4,5,6,7,8,9)) What I need to do is slice out certain 2 dimensional grids that I then want to plot for confidence estimation; make a nice graph. This is fine: as easy as it gets if the 2 dimensions are known. E.g. for the 2nd and 5th axes, one could hardcode something like this: subChi2 = chi2[ ia, ib, :, id, ie, :, ig ] The difficulty is that the user is to state which plane (s)he wants to slice out and we can't code the above. The function itself has to convert 2 rank numbers into an expression for a slice and I can't currently figure out how to do that. There are a huge number of options (well, there are 7*6=42). If one could just manually write a colon into a tuple like (2,2,:,2,2,:,2) or something even like 2,2,0:len(2ndDim),2,2,0:len(5thDim),2, things would be fine. But that doesn't seem to be an option. An alternative would be to set up 2 loops and incrementally read out the individual elements of the slice to a new numpy.zeros ( ( len(2ndDim), len(5thDim) ) ), but again we seem to be diverging from elegance. There must be something that I'm missing. Could somebody have a pointer as to what it is? Thanks in advance, Matt
Try using slice (python builtin) to create slice objects (what is created implicitly by :5, 1:20, etc.). slice takes the same arguments as range. A list of these (7 in your case) can then be passed to A[...] as a tuple. That's how I would do it, but maybe someone else has a better idea or can correct me. --Hoyt On Wed, Aug 6, 2008 at 4:02 PM, Matthew Czesarski <matthew.czesarski@gmail.com> wrote:
Dear list.
I've got a feeling that what I'm trying to do *should* be easy but at the moment I can't find a non-brute-force method.
I'm working with quite a high-rank matrix; 7 dimensions filled with chi**2 values. It's form is something like this:
chi2 = numpy.ones((3,4,5,6,7,8,9))
What I need to do is slice out certain 2 dimensional grids that I then want to plot for confidence estimation; make a nice graph. This is fine: as easy as it gets if the 2 dimensions are known. E.g. for the 2nd and 5th axes, one could hardcode something like this:
subChi2 = chi2[ ia, ib, :, id, ie, :, ig ]
The difficulty is that the user is to state which plane (s)he wants to slice out and we can't code the above. The function itself has to convert 2 rank numbers into an expression for a slice and I can't currently figure out how to do that. There are a huge number of options (well, there are 7*6=42). If one could just manually write a colon into a tuple like (2,2,:,2,2,:,2) or something even like 2,2,0:len(2ndDim),2,2,0:len(5thDim),2, things would be fine. But that doesn't seem to be an option. An alternative would be to set up 2 loops and incrementally read out the individual elements of the slice to a new numpy.zeros ( ( len(2ndDim), len(5thDim) ) ), but again we seem to be diverging from elegance.
There must be something that I'm missing. Could somebody have a pointer as to what it is?
Thanks in advance, Matt
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-- +++++++++++++++++++++++++++++++++++ Hoyt Koepke UBC Department of Computer Science http://www.cs.ubc.ca/~hoytak/ hoytak@gmail.com +++++++++++++++++++++++++++++++++++
Dear Hoyt, Thanks -- that hit the spot! Looks like it was a case of me looking for the answer in the wrong place. Assuming that there would be some sophisticated numpy method -- E.g. A.dim_slice( ( 2,5,... ) ) -- that slices up the array and gives you back the sub-full dimensional matrix you're looking for. But it was there all along as a builtin... Thanks for the pointer, Matt
participants (2)
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Hoyt Koepke -
Matthew Czesarski