Mailman 3 May 2006 - NumPy-Discussion

numpy documentation
by joris＠ster.kuleuven.ac.be 16 May '06

16 May '06

Hi, I've started a numpy documentation wikipage with a list of examples illustrating the use of each numpy function. Although far from complete, you can have a look at what I currently have: http://scipy.org/JorisDeRidder I would like to make the example list more visible for the numpy community, though. Any suggestions? And, needless to say, if anyone likes to contribute, please jump in! Cheers, Joris Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

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bug in argmax (?)
by Pau Gargallo 16 May '06

16 May '06

hi all, argmax gives me some errors for arrays with more than 2 dimensions: >>> import numpy >>> numpy.__version__ '0.9.7.2503' >>> x = numpy.zeros((2,3,4)) >>> x.argmax(0) Traceback (most recent call last): File "<stdin>", line 1, in ? ValueError: bad axis2 argument to swapaxes >>> x.argmax(1) Traceback (most recent call last): File "<stdin>", line 1, in ? ValueError: bad axis2 argument to swapaxes >>> x.argmax(2) array([[0, 0, 0], [0, 0, 0]]) does this happens to anyone else? pau

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Creating small arrays from strings and concatenating with empty arrays
by Bill Baxter 15 May '06

15 May '06

Two quick questions: ---------1------------ Is there any better way to intialize an array from a string than this: A = asarray(matrix("1 2 3")) Or is that as good as it gets? I suppose it's not so inefficient. ----------2------------- A lot of time I'd like to be able to write loops like this: A = array([]) for row in function_that_returns_iterable_of_one_d_arrays(): A = vstack((A,row)) But that generates an error the first iteration because the shape of A is wrong. I could stick in a reshape in the loop: A = array([]) for row in function_that_returns_iterable_of_one_d_arrays(): if not A.shape[0]: A.reshape(0,row.shape[0]) A = vstack((A,row)) Meh, but I don't really like looking at 'if's in loops when I know they're really only going to be true once the first time. In Matlab the empty matrix [] can be concatenated with anything and just takes on it's shape. It's handy for writing code like the above. Thanks, --bill

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Fwd: image.jpg
by Numpy-discussion 15 May '06

15 May '06

--ABX13921.1147716629/adm3.ims.u-tokyo.ac.jp--

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basearray / arraykit
by Tim Hochberg 15 May '06

15 May '06

I created a branch to work on basearray and arraykit: http://svn.scipy.org/svn/numpy/branches/arraykit Basearray, as most of you probably know by now is the array superclass that Travis, Sasha and I have all talked about at various times with slightly different emphasis. Arraykit is something I've mentioned in connection with basearray: a toolkit for creating custom array like objects. Here's a brief example; this is what an custom array class that just supported indexing and shape would look like using arraykit: import numpy.arraykit as _kit class customarray(_kit.basearray): __new__ = _kit.fromobj __getitem__ = _kit.getitem __setitem__ = _kit.setitem shape = property(_kit.getshape, _kit.setshape) In practice you'd probably define a few more things like __repr__, etc, but this should get the idea across. Stuff like the above already works. However there are several areas that need more work: 1. arraykit is very tangled of with multiarraymodule.c. I would like to decouple these. 2. A lot of stuff has an extra layer of python in it. I would like to remove that to minimize the extra overhead custom array types get. 3. The functions that are available closely reflect my particular application. If someone else uses this, they're likely to want functions that are not yet exposed through arraykit. That's it for now. More updates as events warrant. -tim

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Building on Windows
by Stephan Tolksdorf 15 May '06

15 May '06

Hi, there are still some (mostly minor) problems with the Windows build of Numpy (MinGW/Cygwin/MSVC). I'd be happy to produce patches and documentation enhancements, but before doing so I'd like to know if there's interest from one of the core developers to review/commit these patches afterwards. I'm asking because in the past questions and suggestions regarding the building process of Numpy (especially on Windows) often remained unanswered on this list. I realise that many developers don't use Windows and that the distutils build is a complex beast, but the current situation seems a bit unsatisfactory - and I would like to help. Would there be any interest for further refactoring of the build code over and above patching errors? Stephan

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numpy.float64(numpy.NaN)!=numpy.NaN
by Simon Burton 15 May '06

15 May '06

>>> numpy.float64(numpy.NaN)==numpy.NaN False Hmm. Bug or feature ? >>> numpy.__version__ 0.9.7.2502 Simon. -- Simon Burton, B.Sc. Licensed PO Box 8066 ANU Canberra 2601 Australia Ph. 61 02 6249 6940 http://arrowtheory.com

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downsample vector with averaging
by Ryan Krauss 14 May '06

14 May '06

I need to downsample some data while averaging it. Basically, I have a vector and I want to take for example every ten points and average them together so that the new vector would be made up of newvect[0]=oldvect[0:9].mean() newvect[1]=oldevect[10:19].mean() .... Is there a built-in or vectorized way to do this? I default to thinking in for loops, but that can lead to slow code. Thanks, Ryan

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NumPy, Numeric or numarray or all of them?
by Mateusz Łoskot 13 May '06

13 May '06

Hi, I'm a developer contributing to GDAL project (http://www.gdal.org). GDAL is a core GeoSpatial Librarry that has had Python bindings for a while. Recently, we did get some reports from users that GDAL bindings do not work with NumPy package. We've learned on the NumPy website that it's a new derivation from Numeric code base. So, now we are facing the question what we should do? Should we completely port our project to use NumPy or to stay with Numeric for a while (e.g. 1 year). There is also idea to support both packages. Python plays a very important role in GDAL project, so our concern are quite critical for future development. This situation brings some questions we'd like to ask NumPy Dev Team: Is it fair to say we are unlikely to see Numeric releases for new Pythons in the future? Can we consider NumPy as the only package in future? Simply, we are wondering which Python library we should develop for NumPy, Numeric or numarray to be most generally useful. What's the recommended way to go now? We'd appreciate your assistance on this issue. Best regards -- Mateusz Łoskot http://mateusz.loskot.net

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Weird slicing behavior
by David Huard 12 May '06

12 May '06

Hi, I noticed that slicing outside bounds with lists returns the whole list : >>> a = range(5) >>> a[-7:] [0, 1, 2, 3, 4] while in numpy, array has a weird behavior >>> a = numpy.arange(5) >>> a[-7:] array([3, 4]) # Ok, it takes modulo(index, length) but >>> a[-11:] array([0, 1, 2, 3, 4]) # ??? Cheers, David

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