Mailman 3 July 2008 - NumPy-Discussion

A couple of testing issues
by Alan McIntyre 09 Jul '08

09 Jul '08

Hi all, I wanted to point out a couple of things about the new test framework that you should keep in mind if you're writing tests: - Don't use NumpyTestCase any more, just use TestCase (which is available if you do from numpy.testing import *). Using NumpyTestCase now causes a deprecation warning. - Test functions and methods will only be picked up based on name if they begin with "test"; "check_*" will no longer be seen as a test function. I figured I should mention these since there probably hasn't been a general announcement about the testing changes. Thanks, Alan

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Re: [Numpy-discussion] Numpy-discussion Digest, Vol 22, Issue 33
by Catherine Moroney 09 Jul '08

09 Jul '08

> > > 2008/7/9 Catherine Moroney <Catherine.M.Moroney(a)jpl.nasa.gov>: > > > >> I have a question about performing element-wise logical operations > >> on numpy arrays. > >> > >> If "a", "b" and "c" are numpy arrays of the same size, does the > >> following syntax work? > >> > >> mask = (a > 1.0) & ((b > 3.0) | (c > 10.0)) > >> > >> It seems to be performing correctly, but the documentation that > I've > >> read indicates that "&" and "|" are for bitwise operations, not > >> element-by- > >> element operations in arrays. > >> > >> I'm trying to avoid using "logical_and" and "logical_or" because > they > >> make the code more cumbersome and difficult to read. Are "&" > and "|" > >> acceptable substitutes for numpy arrays? > > > > Yes. Unfortunately it is impossible to make python's usual logical > > operators, "and", "or", etcetera, behave correctly on numpy > arrays. So > > the decision was made to use the bitwise operators to express > logical > > operations on boolean arrays. If you like, you can think of boolean > > arrays as containing single bits, so that the bitwise operators > *are* > > the logical operators. > > > > Confusing, but I'm afraid there really isn't anything the numpy > > developers can do about it, besides write good documentation. > > > Do "&" and "|" work on all types of numpy arrays (i.e. floats and > 16 and 32-bit integers), or only on arrays of booleans? The short > tests I've done seem to indicate that it does, but I'd like to have > some confirmation. > > They work for all integer types but not for float or complex types: > > In [1]: x = ones(3) > > In [2]: x | x > ---------------------------------------------------------------------- > ----- > TypeError Traceback (most recent > call last) > > /home/charris/<ipython console> in <module>() > > TypeError: unsupported operand type(s) for |: 'float' and 'float' > > > Comparisons always return boolean arrays, so you don't have to > worry about that. > > Chuck > I've attached a short test program for numpy arrays of floats for which "&" and "|" seem to work. If, as you say, "&" and "|" don't work for floats, why does this program work? from numpy import * a = array([(1.1, 2.1),(3.1, 4.1)],'float') b = a + 1 c = b + 1 print "a = ",a print "b = ",b print "c = ",c mask = (a < 4.5) & (b < 4.5) & (c < 4.5) print "mask = ",mask print "masked a = ",a[mask] print "masked b = ",b[mask] print "masked c = ",c[mask] >

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Re: [Numpy-discussion] Numpy-discussion Digest, Vol 22, Issue 32
by Catherine Moroney 09 Jul '08

09 Jul '08

On Jul 9, 2008, at 10:00 AM, numpy-discussion-request(a)scipy.org wrote: > Send Numpy-discussion mailing list submissions to > numpy-discussion(a)scipy.org > > To subscribe or unsubscribe via the World Wide Web, visit > http://projects.scipy.org/mailman/listinfo/numpy-discussion > or, via email, send a message with subject or body 'help' to > numpy-discussion-request(a)scipy.org > > You can reach the person managing the list at > numpy-discussion-owner(a)scipy.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Numpy-discussion digest..." > Today's Topics: > > 1. Re: element-wise logical operations on numpy arrays > (Anne Archibald) > > From: "Anne Archibald" <peridot.faceted(a)gmail.com> > Date: July 9, 2008 9:35:20 AM PDT > To: "Discussion of Numerical Python" <numpy-discussion(a)scipy.org> > Subject: Re: [Numpy-discussion] element-wise logical operations on > numpy arrays > Reply-To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> > > > 2008/7/9 Catherine Moroney <Catherine.M.Moroney(a)jpl.nasa.gov>: > >> I have a question about performing element-wise logical operations >> on numpy arrays. >> >> If "a", "b" and "c" are numpy arrays of the same size, does the >> following syntax work? >> >> mask = (a > 1.0) & ((b > 3.0) | (c > 10.0)) >> >> It seems to be performing correctly, but the documentation that I've >> read indicates that "&" and "|" are for bitwise operations, not >> element-by- >> element operations in arrays. >> >> I'm trying to avoid using "logical_and" and "logical_or" because they >> make the code more cumbersome and difficult to read. Are "&" and "|" >> acceptable substitutes for numpy arrays? > > Yes. Unfortunately it is impossible to make python's usual logical > operators, "and", "or", etcetera, behave correctly on numpy arrays. So > the decision was made to use the bitwise operators to express logical > operations on boolean arrays. If you like, you can think of boolean > arrays as containing single bits, so that the bitwise operators *are* > the logical operators. > > Confusing, but I'm afraid there really isn't anything the numpy > developers can do about it, besides write good documentation. > Do "&" and "|" work on all types of numpy arrays (i.e. floats and 16 and 32-bit integers), or only on arrays of booleans? The short tests I've done seem to indicate that it does, but I'd like to have some confirmation. > Good luck, > Anne > Catherine > > > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion(a)scipy.org > http://projects.scipy.org/mailman/listinfo/numpy-discussion

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REMINDER: SciPy 2008 Early Registration ends in 2 days
by Jarrod Millman 09 Jul '08

09 Jul '08

Hello, This is a reminder that early registration for SciPy 2008 ends in two days on Friday, July 11th. To register, please see: http://conference.scipy.org/to_register This year's conference has two days for tutorials, two days of presentations, and ends with a two day coding sprint. If you want to learn more see my blog post: http://jarrodmillman.blogspot.com/2008/07/scipy-2008-conference-program-pos… Cheers, -- Jarrod Millman Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/

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element-wise logical operations on numpy arrays
by Catherine Moroney 09 Jul '08

09 Jul '08

Hello, I have a question about performing element-wise logical operations on numpy arrays. If "a", "b" and "c" are numpy arrays of the same size, does the following syntax work? mask = (a > 1.0) & ((b > 3.0) | (c > 10.0)) It seems to be performing correctly, but the documentation that I've read indicates that "&" and "|" are for bitwise operations, not element-by- element operations in arrays. I'm trying to avoid using "logical_and" and "logical_or" because they make the code more cumbersome and difficult to read. Are "&" and "|" acceptable substitutes for numpy arrays? Thanks, Catherine

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Detecting phase windings
by gruben＠bigpond.net.au 09 Jul '08

09 Jul '08

I have a speed problem with the approach I'm using to detect phase wrappings in a 3D data set. In my application, phaseField is a 3D array containing the phase values of a field. In order to detect the vortices/phase windings at each point, I check for windings on each of 3 faces of a 2x2 cube with the following code: def HasVortex(cube): ''' cube is a 2x2x2 array containing the phase values returns True if any of 3 orthogonal faces contains a phase wrapping ''' # extract 3 cube faces - flatten to make indexing easier c1 = cube[0,:,:].flatten() c3 = cube[:,0,:].flatten() c5 = cube[:,:,0].flatten() # now order the phases in a circuit around each face, finishing in the same corner as we began # Use numpy's phase unwrapping code, which has a default threshold of pi u1 = unwrap(c1[cwi]) u3 = unwrap(c3[cwi]) u5 = unwrap(c5[cwi]) # check whether the final unwrapped value coincides with the value in the cell we started at return not allclose(r_[u1[0],u3[0],u5[0]], r_[u1[4],u3[4],u5[4]]) vortexArray = array([int(HasVortex(phaseField[x:x+2,y:y+2,z:z+2])) for x in range(phaseField.shape[0]-1) for y in range(phaseField.shape[1]-1) for z in range(phaseField.shape[2]-1)]\ ).reshape((xs-1,ys-1,zs-1)) Whilst this does what I want, it's incredibly slow. I'm wondering whether anyone has done this before, or can think of a better approach. My thoughts about a better approach are to stack the values along 3 new dimensions making a 6D array and use unwrap along the 3 new dimensions. Something like the following may give you an idea of what I mean, but it's a toy example trying to extend 2D into 3D, rather than 3D into 6D, because I haven't come to grips with enough of numpy's axis reshaping and stacking tricks to avoid getting a severe headache when trying to generalize it: a = array([[1.,3.], [6.,5.]]) b = np.dstack((a, roll(a,-1,axis=1), roll(roll(a,-1,axis=0),-1,axis=1), roll(a,-1,axis=0), a)) print np.unwrap(b, axis=2) A problem with this approach is that the memory requirements for the stacked version will be much greater, so some version using views would be preferable. Any ideas? Gary Ruben

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Documentation: topical docs and reviewing our work
by Stéfan van der Walt 09 Jul '08

09 Jul '08

Hi all, A `numpy.doc` sub-module has been added, which contains documentation for topics such as indexing, broadcasting, array operations etc. These can be edited from the documentation wiki: http://sd-2116.dedibox.fr/pydocweb/doc/numpy.doc/ If you'd like to document a topic that is not there, let me know and I'll add it. Further, we have documented a large number of functions, and the list is growing by the day. If you go to the docstring summary page: http://sd-2116.dedibox.fr/pydocweb/doc/ the ones ready for review are marked in pink, right at the top. Please log in and leave comments on those. Your input would be much appreciated! Regards Stéfan

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Numpy on AIX 5.3
by Marek Wojciechowski 09 Jul '08

09 Jul '08

Dnia poniedziałek 07 lipiec 2008, numpy-discussion-request(a)scipy.org napisał: > > > > File "/home/marek/tmp/numpy-1.1.0/numpy/distutils/ccompiler.py", > > > > line 303, in CCompiler_cxx_compiler > > > > + cxx.linker_so[2:] > > > > TypeError: can only concatenate list (not "str") to list > > > > > > Just by reading at the code, the line > > > > > > [cxx.linker_so[0]] + cxx.compiler_cxx[0] + cxx.linker_so[2:] > > > > > > Cannot work unless cxx.compiler_cxx is a nested list. Since AIX is not > > > that common, it is well possible that this mistake was hidden for a > > > long time. So I would first try something like: > > > > > > cxx.linker_so = [cxx.linker_so[0], cxx.compiler_cxx[0]] > > > > +cxx.linker_so[2:] > > Please apply also the above bugfix to trunk and numpy-1.1, i.e. change cxx.linker_so = [cxx.linker_so[0]] + cxx.compiler_cxx[0] + cxx.linker_so[2:] to cxx.linker_so = [cxx.linker_so[0], cxx.compiler_cxx[0]] + cxx.linker_so[2:] in line 303 of cccompiler.py in distutils. Greetings, -- Marek Wojciechowski

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chararray behavior
by Alan McIntyre 09 Jul '08

09 Jul '08

Since chararray doesn't currently have any tests, I'm writing some, and I ran across a couple of things that didn't make sense to me: 1. The code for __mul__ is exactly the same as that for __rmul__; is there any reason __rmul__ shouldn't just call __mul__? 1.5. __radd__ seems like it doesn't do anything fundamentally different from __add__, is there a reason to have a separate implementation of __radd__? 2. The behavior of __mul__ seems odd: >>> Q=np.chararray((2,2),itemsize=1,buffer='abcd') >>> Q chararray([['a', 'b'], ['c', 'd']], dtype='|S1') >>> Q*3 chararray([['aaa', 'bbb'], ['ccc', 'ddd']], dtype='|S4') >>> Q*4 chararray([['aaaa', 'bbbb'], ['cccc', 'dddd']], dtype='|S4') >>> Q*5 chararray([['aaaa', 'bbbb'], ['cccc', 'dddd']], dtype='|S4') Is it supposed to work this way? Thanks, Alan

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numpy installation issues
by Chris Bartels 09 Jul '08

09 Jul '08

Hi, I have been trying to install numpy (as a dependancy of matplotlib) on cygwin. I would like to use the native cygwin-python numpy install, as the cygwin development environment I use is portable on usb disk ( http://www.dam.brown.edu/people/sezer/software/cygwin/). (Extremely useful!) It would be even better if i can actually install numpy/scipy/matplotlib on this portable distribution. Unfortunately "python setup.py build" does give an error. (I installed python with sources, so /usr/lib/python2.5/config is existing. Which seemed to be a problem with another python package when I googled for this error.) I included the build messages... Anybody any clue? (I know there exists now a windows enthought distribution but I cannot use that as it is not portable and I don't know how to combine it with cygwin. I essentially only need numpy and matplotlib. These should not be impossible to install?) Kind regards, Chris

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NumPy-Discussion July 2008