Mailman 3 June 2007 - NumPy-Discussion

baffled by gfortran
by Mathew Yeates 28 Jun '07

28 Jun '07

I have gfortran installed in my path. But when I run python setup.py build I get Found executable /usr/bin/g77 gnu: no Fortran 90 compiler found gnu: no Fortran 90 compiler found customize GnuFCompiler gnu: no Fortran 90 compiler found gnu: no Fortran 90 compiler found The output of python setup.py config_fc --help-fcompiler shows that gfortran is visible Gnu95FCompiler instance properties: archiver = ['/u/vento0/myeates/bin/gfortran', '-cr'] compile_switch = '-c' compiler_f77 = ['/u/vento0/myeates/bin/gfortran', '-Wall', '-ffixed- form', '-fno-second-underscore', '-fPIC', '-O3', '-funroll -loops', '-march=opteron', '-mmmx', '-m3dnow', '-msse2', ' -msse'] compiler_f90 = ['/u/vento0/myeates/bin/gfortran', '-Wall', '-fno-second -underscore', '-fPIC', '-O3', '-funroll-loops', '- I have tried python setup.py config_fc --fcompiler=gnu95 build etc but I can't figure it out. Can some examples be added to the readme file?

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Re: [Numpy-discussion] Unhandled floating point exception running test in numpy-1.0.3 and svn 3875
by John Ollinger 28 Jun '07

28 Jun '07

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NumPy name? homepage?
by Kevin Christman 26 Jun '07

26 Jun '07

I'm doing a quick cleanup of NumPy on wikipedia. 1. What is the proper name? NumPy or Numerical Python? (I understand that numarray and numeric are older, and now deprecated versions.) If it is NumPy then I would suggest renaming the page to that and then *only* talk about the other (confusing) names in the History of NumPy section. 2. What is the proper homepage for NumPy? Is it numpy.scipy.org or www.scipy.org/numpy or is there another? The page http://www.scipy.org/SciPyDotOrg references both. 3. What is the status of the "copyright" of the screenshots of SciPy/NumPy on the scipy homepage? The SciPy page on wikipedia probably needs a screenshot, or somebody needs to make one and upload it. Thanks, Kevin ____________________________________________________________________________________ TV dinner still cooling? Check out "Tonight's Picks" on Yahoo! TV. http://tv.yahoo.com/

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annoying numpy string to float conversion behaviour
by Torgil Svensson 26 Jun '07

26 Jun '07

Hi Is there a reason for numpy.float not to convert it's own string representation correctly? Python 2.5.1 (r251:54863, Apr 18 2007, 08:51:08) [MSC v.1310 32 bit (Intel)] on win32>>> import numpy >>> numpy.__version__ '1.0.3' >>> numpy.float("1.0") 1.0 >>> numpy.nan -1.#IND >>> numpy.float("-1.#IND") Traceback (most recent call last): File "<pyshell#20>", line 1, in <module> numpy.float("-1.#IND") ValueError: invalid literal for float(): -1.#IND >>> Also, nan and -nan are represented differently for different float to string conversion methods. I guess the added zeros are a bug somewhere. >>> str(nan) '-1.#IND' >>> "%f" % nan '-1.#IND00' >>> str(-nan) '1.#QNAN' >>> "%f" % -nan '1.#QNAN0' This is a problem when floats are stored in text-files that are later read to be numerically processed. For now I use the following to convert the number. special_numbers=dict([('-1.#INF',-inf),('1.#INF',inf), ('-1.#IND',nan),('-1.#IND00',nan), ('1.#QNAN',-nan),('1.#QNAN0',-nan)]) def string_to_number(x): if x in special_numbers: return special_numbers[x] return float(x) if ("." in x) or ("e" in x) else int(x) Is there a simpler way that I missed? Best Regards, //Torgil

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Re: [Numpy-discussion] effectively computing variograms with numpy
by Timothy Hochberg 26 Jun '07

26 Jun '07

On 6/25/07, Hanno Klemm <klemm(a)phys.ethz.ch> wrote: > > Tim, > > Thank you very much, the code does what's it expected to do. > Unfortunately the thing is still pretty slow on large data sets. > This does seem like the kind of thing that there should be a faster way to compute, particularly since you are binning the results up. One approach would be to just bin the data before doing the computation, however, that loses a lot of accuracy. It does seem like there should be some moment-like approach that would allow you to bin the data before you do the computation, computing the first and second moments, or something similar and then computing the results from the binned moments. I don't know that that would work -- it's just a vague hunch. I don't know if I'll have time to try it out, but I thought I would mention it. I will probably now look for ways to calculate the variogram from some random samples of my data. Thanks for the observation regarding the square array, that would have bitten me, later. Your welcome, I hope this helps some. Regards, -- . __ . |-\ . . tim.hochberg(a)ieee.org [SNIP]

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Familiar w/ reduce ?
by Pierre GM 26 Jun '07

26 Jun '07

All, I have a (m_1+1,m_2+1) matrix of 0 and 1, and I need to compute a second matrix U by recurrence. A double-loop as such #............................ U = empty((m_1+1, m_2+1)) U[:,0] = C[:,0] U[0,:] = C[0,:] for i in range(1,m_1+1): for j in range(1,m_2+1): U[i,j] = C[i,j] * (U[i,j-1] + U[i-1,j]) #............................ does the trick, but I suspect there is not a simpler, more elegant solution. Would anybody have any idea ? Thanks a lot in advance, and all my apologies for being a tad lazy... (PS: FYI, it's for a reimplementation of the Kolmogorov-Smirnov test statistic, where ties occur) P.

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Unwanted attachments on the SciPy home page.
by Charles R Harris 25 Jun '07

25 Jun '07

Hi Robert, Some porn has been uploaded to the attachments page at scipy.org Chuck

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average of array containing NaN
by Giorgio F. Gilestro 25 Jun '07

25 Jun '07

I find myself in a situation where an array may contain not-Numbers that I set as NaN. Yet, whatever operation I do on that array( average, sum...) will threat the NaN as infinite values rather then ignoring them as I'd like it'd do. Am I missing something? Is this a bug or a feature? :-)

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Setting element to masked in a masked array previously containing no masked values
by Jesper Larsen 25 Jun '07

25 Jun '07

Hi numpy users, I have a masked array. I am looping over the elements of this array and sometimes want to set a value to missing. Normally this can be done by: myarray.mask[i] = True However the mask attribute is not indexable when there are no existing missing values in the array (it is simply False). In this case I therefore get an error message: myarray.mask[i] = True TypeError: object does not support item assignment Is the best way to solve the problem to do something like this: mask = ma.getmaskarray(myarray) for i in range(n): if blahblah: mask[i] = True myarray = ma.array(myarray, copy=False, mask=mask) or is there a more elegant solution? Does anyone by the way have any pointers to documentation of the masked array features of numpy? I know that it is treated in the numarray manual but it seems like there are some important syntax differences that make this manual of little use in that regard. - Jesper

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effectively computing variograms with numpy
by Hanno Klemm 25 Jun '07

25 Jun '07

Hi, I have an array which represents regularly spaced spatial data. I now would like to compute the (semi-)variogram, i.e. gamma(h) = 1/N(h) \sum_{i,j\in N(h)} (z_i - z_j)**2, where h is the (approximate) spatial difference between the measurements z_i, and z_j, and N(h) is the number of measurements with distance h. However, I only want to calculate the thing along the rows and columns. The naive approach involves two for loops and a lot of searching, which becomes painfully slow on large data sets. Are there better implementations around in numpy/scipy or does anyone have a good idea of how to do that more efficient? I looked around a bit but couldn't find anything. Hanno

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