Mailman 3 September 2006 - NumPy-Discussion

general version of repmat?
by Bill Baxter 23 Sep '06

23 Sep '06

Is there some way to get the equivalent of repmat() for ndim == 1 and ndim >2. For ndim == 1, repmat always returns a 2-d array, instead of remaining 1-d. For ndim >2, repmat just doesn't work. Maybe we could add a 'reparray', with the signature: reparray(A, repeats, axis=None) where repeats is a scalar or a sequence. If 'repeats' is a scalar then the matrix is duplicated along 'axis' that many times. If 'repeats' is a sequence of length N, then A is duplicated repeats[i] times along axis[i]. If axis is None then it is assumed to be (0,1,2...N). Er that's not quite complete, because it doesn't specify what happens when you reparray an array to a higher dimension, like a 1-d to a 3-d. Like reparray([1,2], (2,2,2)). I guess the axis parameter could have some 'newaxis' entries to accomodate that. --bb

2 4

[Numpy-discussion] 非财务经理的财务管理沙盘模拟课程
by "9月28-29日/上海" 23 Sep '06

23 Sep '06

1 0

TPM全员设备维护与管理
by tpm 23 Sep '06

23 Sep '06

1 0

TPM全员设备维护与管理
by tpm 23 Sep '06

23 Sep '06

1 0

Rationale for atleast_3d
by Bill Baxter 23 Sep '06

23 Sep '06

26 weeks, 4 days, 2 hours and 9 minutes ago, Zdeněk Hurák asked why atleast_3d acts the way it does: http://article.gmane.org/gmane.comp.python.numeric.general/4382/match=atlea… He doesn't seem to have gotten any answers. And now I'm wondering the same thing. Anyone have any idea? --bb

4 4

take behaviour has changed
by Christian Kristukat 23 Sep '06

23 Sep '06

Hi, from 1.0b1 to 1.0rc1 the default behaviour of take seems to have changed when omitting the axis argument: In [13]: a = reshape(arange(12),(3,4)) In [14]: take(a,[2,3]) Out[14]: array([2, 3]) In [15]: take(a,[2,3],1) Out[15]: array([[ 2, 3], [ 6, 7], [10, 11]]) Is this intended? Christian

3 5

Question about recarray
by Lionel Roubeyrie 22 Sep '06

22 Sep '06

Hi all, Is it possible to put masked values into recarrays, I need a array with heterogenous types of datas (datetime objects in the first col, all others are float) but with missing values in some records. For the moment, I don't find any solution for that. I have tried with arrays of dtype=object, but I have problem when I want to compute min, max, ... with an error like: TypeError: function not supported for these types, and can't coerce safely to supported types. thanks -- Lionel Roubeyrie - lroubeyrie(a)limair.asso.fr LIMAIR http://www.limair.asso.fr

3 5

reading large files
by Svein-Erik.Hamran＠ffi.no 22 Sep '06

22 Sep '06

I would like to read files >2Gbyte. From earlier posting I believed it should be possible with python 2.5. I am getting MemoryError when trying to read a file larger than 2Gb. I am using python2.5 and numpy1.0rc1. Any solution to the problem? SEH

3 2

Putmask/take ?
by PGM 22 Sep '06

22 Sep '06

Folks, I'm running into the following problem with putmask on take. >>> import numpy >>> x = N.arange(12.) >>> m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1] >>> i = N.nonzero(m)[0] >>> w = N.array([-1, -2, -3, -4.]) >>> x.putmask(w,m) >>> x.take(i) >>> N.allclose(x.take(i),w) False I'm wondering ifit is intentional, or if it's a problem on my build (1.0b5), or if somebody experienced it as well. Thanks a lot for your input. P.

5 5

please change mean to use dtype=float
by Sebastian Haase 22 Sep '06

22 Sep '06

Hello all, I just had someone from my lab coming to my desk saying: "My god - SciPy is really stupid .... An array with only positive numbers claims to have a negative mean !! "? I was asking about this before ... the reason was of course that her array was of dtype int32 and had many large values to cause an overflow (wrap around) . Now that the default for axis is None (for all functions having an axis argument), can we please change dtype to default to float64 !? It is really a very confusing and shocking result to get a negative mean on all positive values. It has been stated here before that numpy should target the "scientist" rather than the programmers ... I would argue that mean() almost always requires the precision of "double" (float64) to produce usable results. Please consider this change before the 1.0 release ... Thanks, Sebastian Haase

9 39

NumPy-Discussion September 2006