Mailman 3 February 2014 - NumPy-Discussion

Custom floating point representation to IEEE 754 double
by Daniele Nicolodi 26 Feb '14

26 Feb '14

Hello, I'm dealing with an instrument that transfers numerical values through an RS232 port in a custom (?) floating point representation (56 bits, 4 bits exponent and 52 bits significand). Of course I need to convert this format to a standard IEEE 754 double to be able to do anything useful with it. I came up with this simple code: def tofloat(data): # 56 bits floating point representation # 4 bits exponent # 52 bits significand d = frombytes(data) l = 56 p = l - 4 e = int(d >> p) + 17 v = 0 for i in xrange(p): b = (d >> i) & 0x01 v += b * pow(2, i - p + e) return v where frombytes() is a simple function that assembles 7 bytes read from the serial port into an integer for easing the manipulation: def frombytes(bytes): # convert from bytes string value = 0 for i, b in enumerate(reversed(bytes)): value += b * (1 << (i * 8)) return value I believe that tofloat() can be simplified a bit, but except optimizations (and cythonization) of this code, there is any simpler way of achieving this? Thanks. Cheers, Daniele

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assigning full precision values to longdouble scalars
by Scott Ransom 26 Feb '14

26 Feb '14

Hi All, So I have a need to use longdouble numpy scalars in an application, and I need to be able to reliably set long-double precision values in them. Currently I don't see an easy way to do that. For example: In [19]: numpy.longdouble("1.12345678901234567890") Out[19]: 1.1234567890123456912 Note the loss of those last couple digits. In [20]: numpy.float("1.12345678901234567890") Out[20]: 1.1234567890123457 In [21]: numpy.longdouble("1.12345678901234567890") - numpy.float("1.12345678901234567890") Out[21]: 0.0 And so internally they are identical. In this case, the string appears to be converted to a C double (i.e. numpy float) before being assigned to the numpy scalar. And therefore it loses precision. Is there a good way of setting longdouble values? Is this a numpy bug? I was considering using a tiny cython wrapper of strtold() to do a conversion from a string to a long double, but it seems like this is basically what should be happening internally in numpy in the above example! Thanks, Scott -- Scott M. Ransom Address: NRAO Phone: (434) 296-0320 520 Edgemont Rd. email: sransom(a)nrao.edu Charlottesville, VA 22903 USA GPG Fingerprint: 06A9 9553 78BE 16DB 407B FFCA 9BFA B6FF FFD3 2989

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shortcut nonzero?
by Alan G Isaac 25 Feb '14

25 Feb '14

Is there a shortcut version for finding the first (k) instance(s) of nonzero entries? I'm thinking of Matlab's `find(X,k)`: http://www.mathworks.com/help/matlab/ref/find.html Easy enough to write of course. I thought `flatnonzero` would be the obvious place for this, but it does not have a `first=k` option. Is such an option worth suggesting? Thanks, Alan Isaac

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cPickle.loads and Numeric
by Chris 25 Feb '14

25 Feb '14

I have some old code that uses cPickle.loads which used to work, but now reports an error in loading the module Numeric. Since Numeric has been replaced by numpy, this makes sense, but, how can I get cPickle.loads to work? I tested the code again on an older machine and it works fine there, but, I'd like to get it working again on a modern set-up as well. Thanks!

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numpy.random.geometric is shifted
by Alan G Isaac 25 Feb '14

25 Feb '14

Just got momentarily snagged by not checking the excellent documentation, which clearly says that numpy provides the shifted geometric. I'm wondering why? Who else does? (Not Mathematica, Matlab, Maple, or Octave.) Thanks, Alan Isaac

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ANN: SfePy 2014.1
by Robert Cimrman 25 Feb '14

25 Feb '14

I am pleased to announce release 2014.1 of SfePy. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by the finite element method. The code is based on NumPy and SciPy packages. It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: http://groups.google.com/group/sfepy-devel Git (source) repository, issue tracker, wiki: http://github.com/sfepy Highlights of this release -------------------------- - sfepy.fem was split to separate FEM-specific and general modules - lower memory usage by creating active DOF connectivities directly from field connectivities - new handling of field and variable shapes - clean up: many obsolete modules were removed, all module names follow naming conventions For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1 (rather long and technical). Best regards, Robert Cimrman and Contributors (*) (*) Contributors to this release (alphabetical order): Vladimír Lukeš, Matyáš Novák, Jaroslav Vondřejc

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flatnonzero fails with lists
by Alan G Isaac 25 Feb '14

25 Feb '14

I was surprised that `flatnonzero` fails with lists because it calls the `ravel` method, which they do not have, instead of using the `ravel` function. I do not know that there is any policy that NumPy functions should always work on lists, but I have gotten used to them doing so. So I'm just asking, is this intentional? (version 1.7.1) Thanks, Alan Isaac

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Re: [Numpy-discussion] 1.8.1 release
by RayS 25 Feb '14

25 Feb '14

When will we see a http://sourceforge.net/projects/numpy/files/NumPy/1.8.1/Changelog/download changelog? I'd like to get this into our organization's SRS, and a list of fixes (related or not) would be great. - Ray

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recfunctions
by Charles R Harris 25 Feb '14

25 Feb '14

Hi All, Does anyone recall if it was a deliberate choice to not expose recfunctions in the lib package? This is apropos issue #4242<https://github.com/numpy/numpy/issues/4242> . Chuck

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Re: [Numpy-discussion] 1.8.1 release
by RayS 24 Feb '14

24 Feb '14

Has anyone alerted C Gohlke? http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy - Ray

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