Mailman 3 April 2006 - NumPy-Discussion

Unexpected change of array used to index another array
by Webb Sprague April 10, 2006

April 10, 2006

Hi. I indexed an 10 x 10(called bigM below) with another array (OFFS_TMP below). I suppose because OFFS_TMP has negative numbers, it was changed to cycle around to 9 wherever there is a negative 1 (which is the forward version of -1 if you are a 10 x 10 matrix). You can analogous behavior with -2 => 8, etc. Is changing the indexing matrix really the correct behavior? The result of using the index seems to be fine. Has this story been told already and I didn't know it? Below is my ipython session. In [57]: OFFS_TMP Out[57]: array([[-1, 1], [ 0, 1], [ 1, 1], [-1, 0], [ 0, 0], [ 1, 0], [-1, -1], [ 0, -1], [ 1, -1]]) In [58]: bigM[OFFS_TMP] Out[58]: array([[[False, True, False, False, True, False, True, True, True, False], [False, True, False, True, True, False, False, False, True, True]], [[True, False, True, False, True, True, False, False, False, True], [False, True, False, True, True, False, False, False, True, True]], [[False, True, False, True, True, False, False, False, True, True], [False, True, False, True, True, False, False, False, True, True]], [[False, True, False, False, True, False, True, True, True, False], [True, False, True, False, True, True, False, False, False, True]], [[True, False, True, False, True, True, False, False, False, True], [True, False, True, False, True, True, False, False, False, True]], [[False, True, False, True, True, False, False, False, True, True], [True, False, True, False, True, True, False, False, False, True]], [[False, True, False, False, True, False, True, True, True, False], [False, True, False, False, True, False, True, True, True, False]], [[True, False, True, False, True, True, False, False, False, True], [False, True, False, False, True, False, True, True, True, False]], [[False, True, False, True, True, False, False, False, True, True], [False, True, False, False, True, False, True, True, True, False]]], dtype=bool) In [59]: OFFS_TMP Out[59]: array([[9, 1], [0, 1], [1, 1], [9, 0], [0, 0], [1, 0], [9, 9], [0, 9], [1, 9]])

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Speed up function on cross product of two sets?
by Zachary Pincus April 9, 2006

April 9, 2006

Hi folks, I have a inner loop that looks like this: out = [] for elem1 in l1: for elem2 in l2: out.append(do_something(l1, l2)) result = do_something_else(out) where do_something and do_something_else are implemented with only numpy ufuncs, and l1 and l2 are numpy arrays. As an example, I need to compute the median distance from any element in one set to any element in another set. What's the best way to speed this sort of thing up with numpy (e.g. push as much down into the underlying C as possible)? I could re- write do_something with the numexpr tools (which are very cool), but that doesn't address the fact that I've still got nested loops living in Python. Perhaps there's some way in numpy to make one big honking array that contains all the pairs from the two lists, and then just run my do_something on that huge array, but that of course scales poorly. Any thoughts? Zach

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Fw: numpy-discussion
by lbirvyx April 8, 2006

April 8, 2006

----- Original Message ----- From: Burks Aileen To: itwymeyq(a)acecannon.com Sent: Saturday, April 08, 2006 10:37 AM Subject: numpy-discussion

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AD运用EXCEL促进市场营销与财务管理
by 4月15-16上海/21-22深圳 April 7, 2006

April 7, 2006

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Silly array question
by Webb Sprague April 7, 2006

April 7, 2006

In R, if you have an Nx2 array of integers, you can use that to index an TxS array, yielding a 1xN result. Is there a way to do that in numpy? I looked for a pairs function but I coudn't find it, vaguely remembering that might be around... I know it would be a trivial loop to write, but a numpy array function would be faster (I hope). Example I = [[0,0], [1,1], [2,2], [1,1]] M = [[1, 2, 3, 4], [5, 6, 7, 8], [9,10,11, 12], [13, 14, 15, 16]] M[I] = [1,6,11,6]. Thanks!

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Fw: numpy-discussion
by mxjmfen April 6, 2006

April 6, 2006

----- Original Message ----- From: Rosenberg Kris To: xqoahbphlic(a)time.net Sent: Friday, April 07, 2006 11:26 AM Subject: numpy-discussion

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Newbie indexing question and print order
by amcmorl April 6, 2006

April 6, 2006

Hi all, I'm having a bit of trouble getting my head around numpy's indexing capabilities. A quick summary of the problem is that I want to lookup/index in nD from a second array of rank n+1, such that the last (or first, I guess) dimension contains the lookup co-ordinates for the value to extract from the first array. Here's a 2D (3,3) example: In [12]:print ar [[ 0.15 0.75 0.2 ] [ 0.82 0.5 0.77] [ 0.21 0.91 0.59]] In [24]:print inds [[[1 1] [1 1] [2 1]] [[2 2] [0 0] [1 0]] [[1 1] [0 0] [2 1]]] then somehow return the array (barring me making any row/column errors): In [26]: c = ar.somefancyindexingroutinehere(inds) In [26]:print c [[ 0.5 0.5 0.91] [ 0.59 0.15 0.82] [ 0.5 0.15 0.91]] i.e. c[x,y] = a[ inds[x,y,0], inds[x,y,1] ] Any suggestions? It looks like it should be relatively simple using 'put' or 'take' or 'fetch' or 'sit' or something like that, but I'm not getting it. While I'm here, can someone help me understand the rationale behind 'print' printing row, column (i.e. a[0,1] = 0.75 in the above example rather than x, y (=column, row; in which case 0.75 would be in the first column and second row), which seems to me to be more intuitive. I'm really enjoying getting into numpy - I can see it'll be simpler/faster coding than my previous environments, despite me not knowing my way at the moment, and that python has better opportunities for extensibility. So, many thanks for your great work. -- Angus McMorland email a.mcmorland(a)auckland.ac.nz mobile +64-21-155-4906 PhD Student, Neurophysiology / Multiphoton & Confocal Imaging Physiology, University of Auckland phone +64-9-3737-599 x89707 Armourer, Auckland University Fencing Secretary, Fencing North Inc.

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New patch for MA
by Pierre GM April 6, 2006

April 6, 2006

Folks, You can find a new patch for MA on the wiki http://projects.scipy.org/scipy/numpy/attachment/wiki/MaskedArray/ma-200603… along with a test suite. The 'clip' method should now work with array arguments. Were also added cumsum, cumprod, std, var and squeeze. I'll deal with flags, setflags, setfield, dump and others when I'll have a better idea of how it works -- which probably won't happen anytime soon, as I don't really have time to dig in the code for these functions. AAMOF, I'm more interested in checking/patching some other aspects of numpy for MA (eg, mlab...) Once again, please send me your comments and suggestions. Thx for everything P.

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What is diagonal for nd>2?
by Sasha April 6, 2006

April 6, 2006

It looks like the definition of the diagonal changed somewhere between Numeric 24.0 and numpy: In Numeric: >>> x = Numeric.arange(2*4*4) >>> x = Numeric.reshape(x, (2, 4, 4)) >>> x array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]], [[16, 17, 18, 19], [20, 21, 22, 23], [24, 25, 26, 27], [28, 29, 30, 31]]]) >>> Numeric.diagonal(x) array([[ 0, 5, 10, 15], [16, 21, 26, 31]]) But in numpy: >>> import numpy as Numeric >>> x = Numeric.arange(2*4*4) >>> x = Numeric.reshape(x, (2, 4, 4)) >>> Numeric.diagonal(x) array([[ 0, 20], [ 1, 21], [ 2, 22], [ 3, 23]]) The old logic seems to be clear: x is a pair of matrices and diagonal returns a pair of diagonals, but the new logic seems unclear: the disagonal returns the first rows of the two matrices transposed. Does anyone know when this change was introduced and why?

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A random.normal function with stdev as array
by Eric Emsellem April 6, 2006

April 6, 2006

Hi, I am trying to optimize a code where I derive random numbers many times and having an array of values for the stdev parameter. I wish to have an efficient way of doing something like: ################## stdev = array([1.1,1.2,1.0,2.2]) result = numpy.zeros(stdev.shape, Float) for i in range(len(stdev)) : result[i] = numpy.random.normal(0, stdev[i]) ################## In my case, stdev can in fact be an array of a few millions floats... so I really need to optimize things. Any hint on how to code this efficiently ? And in general, where could I find tips for optimizing a code where I unfortunately have too many loops such as "for i in range(Nbody) : " with Nbody being > 10^6 ? thanks! Eric

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