NumPy-Discussion May 2006

numpy-discussion@python.org

115 participants
146 discussions

Power with negative exponent
by Ivan Vilata i Balaguer 21 May '06

21 May '06

Hi all, when working with numexpr, I have come across a curiosity in both numarray and numpy:: In [30]:b = numpy.array([1,2,3,4]) In [31]:b ** -1 Out[31]:array([1, 0, 0, 0]) In [32]:4 ** -1 Out[32]:0.25 In [33]: According to http://docs.python.org/ref/power.html: For int and long int operands, the result has the same type as the operands (after coercion) unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. Then, shouldn’t be ``b ** -1 == array([1.0, 0.5, 0.33333333, 0.25])``? Is this behaviour intentional? (I googled for previous messages on the topic but I didn’t find any.) Thanks, :: Ivan Vilata i Balaguer >qo< http://www.carabos.com/ Cárabos Coop. V. V V Enjoy Data ""

1 0

casting homogeneous struct arrays
by Simon Burton 21 May '06

21 May '06

This is something I will need to be able do: >>> a=numpy.array( [(1,2,3)], list('lll') ) >>> a.astype( 'l' ) Traceback (most recent call last): File "<stdin>", line 1, in ? TypeError: an integer is required (and what a strange error message). Is there a workaround ? .tostring/.fromstring incurs a memcopy copy, if i am not mistaken. Simon. -- Simon Burton, B.Sc. Licensed PO Box 8066 ANU Canberra 2601 Australia Ph. 61 02 6249 6940 http://arrowtheory.com

2 2

Wetsuit
by winnie 21 May '06

21 May '06

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numpy and mayavi
by Emanuele Olivetti 20 May '06

20 May '06

I need to use a 3D visualization toolkit together with the amazing numpy. On the scipy website mayavi/VTK are suggested but as far as I tried, mayavi uses only Numeric and doesn't like numpy arrays (and mayavi, last release, dates 13 September 2005 so a bit earlier for numpy). Do you know something about it or could suggest alternative 3D visualization packages? Basically my needs are related to this task: I've 3D matrices and I'like to see them not only as 2D slices. Thanks in advance, Emanuele

3 2

implicit coersion int8 -> int16 ?
by Simon Burton 20 May '06

20 May '06

>>> a=numpy.array([1,2,3],numpy.Int8) >>> a array([1, 2, 3], dtype=int8) >>> a*2 array([2, 4, 6], dtype=int16) >>> My little 2 lives happily as an int8, so why is the int8 array cast to an int16 ? I spent quite a while finding this bug. Furthermore, this works: >>> a*a array([1, 4, 9], dtype=int8) but not this: >>> a*numpy.array(2,dtype=numpy.Int8) array([2, 4, 6], dtype=int16) >>> print numpy.__version__ 0.9.9.2533 Simon. -- Simon Burton, B.Sc. Licensed PO Box 8066 ANU Canberra 2601 Australia Ph. 61 02 6249 6940 http://arrowtheory.com

2 2

ascii input/output cookbook doc
by joris＠ster.kuleuven.ac.be 20 May '06

20 May '06

For your information: I just created a small cookbook document http://scipy.org/Cookbook/InputOutput where it is explained how one can read and write Numpy arrays in human readable (ascii) format. The document describes how one can use read_array/write_array if SciPy is installed, or how one can use load/save if Matplotlib is installed. When neither of these two packages is installed, one basically has no other choice then to improvise, so I also give here a few examples how one could do this. Imho, there is something unsatisfactorily about this need to improvise. Ascii input/output of numpy arrays seems to me a very basic need. Even when one defines Numpy crudely as the N-dimensional array object, and Scipy as the science you can do with these array objects, then I would intuitively still expect that ascii input/output would belong to Numpy rather than to Scipy. There are Numpy support functions for binary format, and for pickled format, but strangely enough not really for ascii format. tofile() and fromfile() do not preserve the shape of a 2d array, and are in practice therefore hardly usable. There may be a signficant fraction of Numpy users that do not need SciPy for their work, and have only Numpy installed. My guess is that the read_array and write_array functions have already been re-invented many many times by these users. Imho, I therefore think that Numpy deserves its own read_array/write_array method. Does anyone else have this feeling, or am I the only one? :o) Joris Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

2 1

Updates to "Guide to NumPy"
by Travis Oliphant 20 May '06

20 May '06

Owners of "Guide to NumPy": I'm a little late with updates. I really apologize for that. It's due to not having a good system in place to allow people to get the updates in a timely manner. The latest version is from March. It includes a bit more information about writing C-extensions and some corrections. I've been sending them to people who urgently need them and request it. I'm working on an update to go along with 0.9.8 release. When it is ready I will send a location and password for people to download it. I expect 2-3 weeks for its release. If you need the March copy earlier than that, please let me know and I'll send you a personal update. Thanks for your patience. I hope to send updates to the manual at major releases of NumPy. Best regards, -Travis Oliphant

1 0

Guide to NumPy latest?
by Mark Heslep 20 May '06

20 May '06

So is the January '06 version of the Guide to Numpy still the most recent version? Mark

4 3

a.squeeze() for 1d and 0d arrays
by Ed Schofield 19 May '06

19 May '06

Hi all, I've discovered a bug in my SciPy code for sparse matrix slicing that was caused by the following behaviour of squeeze(): >>> a = array([3]) # array of shape (1,) >>> type(a.squeeze()) <type 'int32scalar'> That is, squeezing a 1-dim array returns an array scalar. Could we change this to return a 0-dim array instead? Another related question is this: >>> b = array(3) # 0-dim array >>> type(a.squeeze()) <type 'int32scalar'> I find this behaviour surprising too; the docstring claims that squeeze eliminates any length-1 dimensions, but a 0-dimensional array has shape (), without any length-1 dimensions. So shouldn't squeeze() leave 0-dimensional arrays alone? -- Ed

1 0

运用EXCEL促进市场营销和财务管理(AD)
by 6月3-4日上海 19 May '06

19 May '06

1 0

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NumPy-Discussion May 2006