Mailman 3 June 2006 - NumPy-Discussion

matlab translation
by Mathew Yeates 28 Jun '06

28 Jun '06

This is probably in an FAQ somewhere but ..... Is there a tool out there for translating Matlab to Numeric? I found a 1999 posting by Travis asking the same thing! It doesn't seem like it would be all THAT difficult to write. Mathew

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fread codes versus numpy types
by Glen W. Mabey 28 Jun '06

28 Jun '06

Hello, I see the following character codes defined in scipy (presumably) for use with scipy.io.fread() : In [20]:scipy.Complex Out[20]:'D' In [21]:scipy.Complex0 Out[21]:'D' In [22]:scipy.Complex128 Out[22]:'G' In [23]:scipy.Complex16 Out[23]:'F' In [24]:scipy.Complex32 Out[24]:'F' In [25]:scipy.Complex64 Out[25]:'D' In [26]:scipy.Complex8 Out[26]:'F' Then I see the following scalar types also defined: In [27]:scipy.complex64 Out[27]:<type 'complex64scalar'> In [28]:scipy.complex128 Out[28]:<type 'complex128scalar'> In [29]:scipy.complex256 Out[29]:<type 'complex256scalar'> which correspond to types that exist within the numpy module. These names seem to conflict in that (unless I misunderstand what's going on) scipy.complex64 actually occupies 64 bits of data (a 32-bit float for each of {real, imag}) whereas scipy.Complex64 looks like it occupies 128 bits of data (a 64-bit double for each of {real, imag}). Is there something I'm missing, or is this a naming inconsistency? Glen

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indexing bug in numpy r2694
by Keith Goodman 28 Jun '06

28 Jun '06

>> x = asmatrix(rand(3,2)) >> y = asmatrix(rand(3,1)) >> y matrix([[ 0.77952062], [ 0.97110465], [ 0.77450218]]) >> idx = where(y > 0.5)[0] >> idx matrix([[0, 1, 2]]) >> x[idx,:] matrix([[ 0.24837887, 0.52988253], [ 0.28661085, 0.43053076], [ 0.05360893, 0.22668509]]) So far everything works as it should. Now the problem: >> y[idx,:] --------------------------------------------------------------------------- exceptions.ValueError Traceback (most recent call last) /usr/local/lib/python2.4/site-packages/numpy/core/defmatrix.py in __getitem__(self, index) 120 121 def __getitem__(self, index): --> 122 out = N.ndarray.__getitem__(self, index) 123 # Need to swap if slice is on first index 124 retscal = False /usr/local/lib/python2.4/site-packages/numpy/core/defmatrix.py in __array_finalize__(self, obj) 116 self.shape = (1,1) 117 elif ndim == 1: --> 118 self.shape = (1,self.shape[0]) 119 return 120 ValueError: total size of new array must be unchanged And, on a related note, shouldn't this be a column vector? >> x[idx,0] matrix([[ 0.24837887, 0.28661085, 0.05360893]])

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Re: [Numpy-discussion] Numpy Benchmarking
by joris＠ster.kuleuven.ac.be 28 Jun '06

28 Jun '06

Hi, [TO]: NumPy uses Numeric's old wrapper to lapack algorithms. [TO]: [TO]: SciPy uses it's own f2py-generated wrapper (it doesn't rely on the [TO]: NumPy wrapper). [TO]: [TO]: The numpy.dual library exists so you can use the SciPy calls if the [TO]: person has SciPy installed or the NumPy ones otherwise. It exists [TO]: precisely for the purpose of seamlessly taking advantage of [TO]: algorithms/interfaces that exist in NumPy but are improved in SciPy. This strikes me as a little bit odd. Why not just provide the best-performing function to both SciPy and NumPy? Would NumPy be more difficult to install if the SciPy algorithm for inv() was incorporated? Joris Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

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what happened to numarray type names ?
by Simon Burton 28 Jun '06

28 Jun '06

>>> import numpy >>> numpy.__version__ '0.9.9.2631' >>> numpy.Int32 Traceback (most recent call last): File "<stdin>", line 1, in ? AttributeError: 'module' object has no attribute 'Int32' >>> This was working not so long ago. 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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matlab -> python translation
by Stefan van der Walt 28 Jun '06

28 Jun '06

Hi all, I recently saw discussions on the list regarding Matlab/Octave to Python translation. I brought this under John Eaton's attention (he is the original author of Octave) -- below is his response. Regards Stéfan ----- Forwarded message from "John W. Eaton" <jwe(a)bevo.che.wisc.edu> ----- From: "John W. Eaton" <jwe(a)bevo.che.wisc.edu> On 21-Jun-2006, Stefan van der Walt wrote: | I'd like to bring this thread under your attention, in case you want | to comment: | | http://aspn.activestate.com/ASPN/Mail/Message/numpy-discussion/3174978 Would you please pass along the following comments? Translating the syntax might not be too hard, but to have a really effective tool, you have to get all the details of the Matlab/Octave function calls the same as well. So would you do that by linking to Octave's run-time libraries as well? That could pobably be made to work, but it would probably drag in a lot more code that some poeple would expect when they just want to translate and run a relatively small number of lines of Matlab code. Another semantic detail that would likely cause trouble is the (apparent) pass-by-value semantics of Matlab. How would you reconcile this with the mutable types of Python? Finally, I would encourage anyone who wants to work on a Matlab/Octave to Python translator using Octave's parser and run-time libraries to work on this in a way that can be integrated with Octave. Please consider discuss your ideas about this project on the maintainers(a)octave.org mailing list. Thanks, jwe ----- End forwarded message -----

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trigger serotonin empathy excess
by declared 28 Jun '06

28 Jun '06

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int64 wierdness
by EI 28 Jun '06

28 Jun '06

Hi, I'm running python 2.4 on a 64bit linux and get strange results: (int(9))**2 is equal to 81, as it should, but (int64(9))**2 is equal to 0 Is it a bug or a feature? Eugene

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Numpy Benchmarking
by Dennis V. Perepelitsa 28 Jun '06

28 Jun '06

Hi, all. I've run some benchmarks comparing the performance of scipy, numpy, Numeric and numarray vs. MATLAB. There's also the beginnings of a benchmark framework included. The results are online at: http://web.mit.edu/jonas/www/bench/ They were produced on a Thinkpad T42 with an Intel Pentium M 1.7GHz processor running Ubuntu Dapper Drake (6.06). All the languages/packages were built from source, and, in the case of numpy and scipy, linked to ATLAS. Each datapoint represents the arithmetic mean of ten trials. The results have some interesting implications. For example, numpy and scipy perform approximately the same except when it comes to matrix inversion, MATLAB beats out all the Python packages when it comes to matrix addition, and numpy seems to be beaten by its predecessors in some cases. Why is this the case? What are some other, additional benchmarks I could try? Dennis V. Perepelitsa MIT Class of 2008, Course VIII and XVIII-C Picower Institute for Learning and Memory

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Re: [Numpy-discussion] (no subject)
by kanako 28 Jun '06

28 Jun '06

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