Mailman 3 February 2006 - NumPy-Discussion

Re: [Numpy-discussion] subclassing ndaray
by Travis Oliphant 27 Feb '06

27 Feb '06

Stefan van der Walt wrote: >If I understand correctly, the __array_finalize__ method should copy >all meta data from the parent object to the child. In other words, I >might have something like: > >def __init__(self, ...): > self.v = 3 > >def __array_finalize__(self, parent): > self.v = parent.v > >I do not want to do something like > >def __array_finalize__(self, parent): > self.v = 3 > >Because then, every time I do an operation on my array, self.v will be >reset. Shouldn't array_finalize look for such methods/properties and copy >them automatically? > You need to set up __array_finalize__ to do that. I did not want to do that for every sub-class because different things are required for different sub classes. So, this seems like a workable compromise. It should work a bit better now that I've changed it so that __array_finalize__ is called on every sub-class creation. If there is no "parent" then parent will be None. -Travis

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Unexpected property of ndarray.fill
by Sasha 27 Feb '06

27 Feb '06

The function ndarray.fill is documented as taking a scalar value, but in the current implementation it accepts arrays as well and ignores all but the first element Example 1: >>> x = empty(10) >>> x.fill([1,2]) >>> x array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) Example 2: >>> y = empty((2,3)) >>> y.fill([1,2,3]) >>> y array([[1, 1, 1], [1, 1, 1]]) I believe this somewhat unexpected. I would expect one of the following to apply: 1. Exception in both examples 2. Exception in Example 1 and array([[1,2,3], [1,2,3]]) in Example 2 3. array([1, 2, 1, 2, 1, 2, 1, 2, 1, 2]) in Example 1 and array([[1,2,3], [1,2,3]]) in Example 2

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object members
by Martin Wiechert 27 Feb '06

27 Feb '06

Hi developers, any plans on when object members will be back? a = ndarray (shape = (10,), dtype = {'names': ['x'], 'formats': ['|O4']}) TypeError: fields with object members not yet supported. in numpy 0.9.5. Of course, this is better than the segfault in 0.9.4, but it would be quite inconvenient for my project to not have object members. My C code still produces dtypes with object members. Can I safely use them, as long as I make sure new arrays are properly initialised? Thanks, Martin.

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Sparse matrix hooks
by Ed Schofield 26 Feb '06

26 Feb '06

I'm trying to improve integration between SciPy's sparse matrices and NumPy's dense array/matrix objects. One problem I'm facing is that NumPy matrices redefine the * operator to call NumPy's dot() function. Since dot() has no awareness of SciPy's sparse matrix objects, this doesn't work for the operation 'dense * sparse'. (It does work for sparse * dense, which calls sparse.__mul__ first.) I'd like to propose the addition of a basic sparse matrix object to NumPy. This wouldn't need to provide any actual functionality, but could instead provide a skeletal interface or base class from which sparse matrices in other packages (SciPy and potentially PySparse) could derive. The spmatrix object in SciPy would be a good starting point. The benefit of this would be that hooks for proper handling of sparse matrices would be easy to provide for functions like dot(), where(), and var(). There may be other ways to make 'dense * sparse' work in SciPy, but I haven't been able to come up with any. This solution would at least be quite flexible and quite straightforward. -- Ed

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Triangular window function
by Albert Strasheim 26 Feb '06

26 Feb '06

Hello all NumPy already has Hamming, Hanning and some other window functions. It would be useful if the triangular window in SciPy could also be brought over. Any thoughts? Regards Albert

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Fwd: [Numpy-discussion] Re: inconsistent use of axis= keyword argument?
by Nils Wagner 25 Feb '06

25 Feb '06

--- the forwarded message follows ---

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Why multiple ellipses?
by Sasha 25 Feb '06

25 Feb '06

Numpy allows multiple ellipses in indexing expressions, but I am not sure if that is useful. AFAIK, ellipsis stands for "as many :'s as needed", but if there is more than one, how do I know how many :'s each of them represents: >>> x = arange(8) >>> x.shape=(2,2,2) >>> x[0,...,0,...] array([0, 1]) >>> x[0,0,:] array([0, 1]) >>> x[0,:,0] array([0, 2]) In the example above, the first ellipsis represents no :'s and the last one represents one. Is that the current rule that the last ellipsis represents all the needed :'s? What is the possible use for that?

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Windows Build with optimized libraries
by Albert Strasheim 24 Feb '06

24 Feb '06

Hello all I've got the latest NumPy from SVN building with optimized ATLAS 3.7.11 and CLAPACK on Windows. I've also replaced the CLAPACK functions that are provided by ATLAS with the ATLAS ones. The current page on building with Windows: http://www.scipy.org/Installing_SciPy/Windows doesn't have instructions to do this, so I'd like to add info on building with MinGW, Visual Studio .NET 2003, Visual C++ Toolkit 2003 and Visual C++ 2005 Express Edition (if I can figure out how to make distutils detect Visual C++ 2005). I had to change the build scripts in a few places to get things to work. I've attached the patch if someone is interested in committing it to SVN. Briefly, I did the following: 1. Built ATLAS 3.7.11 with Cygwin. I copied libatlas.a as atlas.lib and libcblas.a as cblas.lib to some directory, say c:\tmp\numpylibs. 2. Built CLAPACK 3.0 for Windows with Visual Studio .NET 2003. I added cblaswr.c to clapack.lib and disabled building of the other projects, except for libI77 and libF77. I also changed the project properties of these three projects to use SSE2 instructions (under C/C++ | Code Generation | Enable Enhanced Instruction Set). I don't know if this makes much difference though (anybody have some benchmarks?). 3. I then took release builds of clapack.lib, libF77.lib and libI77.lib and rolled them together with ATLAS's liblapack.a: cp clapack.lib lapack.lib ar x liblapack.a mkdir Release ar x libI77.lib ar x libF77.lib ar r lapack.lib Release/*.obj *.o This adds the symbols from libI77 and libF77 to the library and replaces any existing symbols with the symbols from the ATLAS LAPACK library. I copied this lapack.lib to c:\tmp\numpylibs. 4. I created the file numpy\numpy\distutils\site.cfg with contents: [atlas] library_dirs = c:\tmp\numpylibs atlas_libs = cblas,atlas [lapack] library_dirs = c:\tmp\numpylibs lapack_libs = lapack 5.1. Visual Studio: python setup.py bdist_wininst 5.2. MinGW: python setup.py config --compiler=mingw32 build --compiler=mingw32 bdist_wininst That's it. The build generated a shiny numpy-0.9.6.2168.win32-py2.4.exe. A quick question: it seems that NumPy can also use FFTW 2.1.5 to speed up its FFT functions. Is this the case? If so, I'll take a look at building FFTW 2.1.5 on Windows too. fftw.org's links to solution files for 2.1.3 are broken, so I'll probably have to make new ones. Hope this helps. Regards Albert

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ndarray - reshaping a sub-class
by Colin J. Williams 24 Feb '06

24 Feb '06

The function reshape, when applied to an instance of a sub-class, returns an array instance. The method reshape returns and instance of the sub-class. It seems desirable that both be treated in the same way. Colin W.

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Re: [Numpy-discussion] Re: inconsistent use of axis= keyword argument?
by Robert Kern 24 Feb '06

24 Feb '06

Nils Wagner wrote: > Robert Kern wrote: > >>Vidar Gundersen wrote: >> >> >> >>>and i also wonder why concatenate can't be used to stack 1-d >>>arrays on top of each other, returning a 2-d array? >>> >> >>Use vstack() for that. Also note its companions, hstack() and column_stack(). >> >> > > > Hi Robert, > > Is it possible to use aliases for these operators ? > > In textbooks (Matrix Algebra by Abadir and Magnus, Cambridge University > Press (2005)) you will find > > The vec-operator transforms a matrix into a vector by stacking its > columns one underneath the other. Ask on the list, not private email. -- Robert Kern robert.kern(a)gmail.com "In the fields of hell where the grass grows high Are the graves of dreams allowed to die." -- Richard Harter --_===36437881====uni-stuttgart.de===_--

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