Mailman 3 December 2006 - NumPy-Discussion

Definition of correlation, correlate and so on ?
by David Cournapeau Dec. 13, 2006

Dec. 13, 2006

Hi, I am polishing some code to compute autocorrelation using fft, and when testing the code against numpy.correlate, I realised that I am not sure about the definition... There are various function related to correlation as far as numpy/scipoy is concerned: numpy.correlate numpy.corrcoef scipy.signal.correlate For me, the correlation between two sequences X and Y at lag t is the sum(X[i] * Y*[i+lag]) where Y* is the complex conjugate of Y. numpy.correlate does not use the conjugate, scipy.signal.correlate as well, and I don't understand numpy.corrcoef. I've never seen complex correlation used without the conjugate, so I was curious why this definition was used ? It is incompatible with the correlation as a scalar product, for example. Could someone give the definition used by those function ? cheers, David

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a.T
by Charles R Harris Dec. 13, 2006

Dec. 13, 2006

Hi all, I'm curious about the error thrown when I use a.T as the left side of a multiply assign. In the following, I am multiplying each the rows of 'a' by the corresponding element of arange(n), i.e., broadcasting from the left. The result looks fine, but an error is thrown after the operation is complete. In [62]: a = arange(12).reshape(3,2,2) In [63]: a Out[63]: array([[[ 0, 1], [ 2, 3]], [[ 4, 5], [ 6, 7]], [[ 8, 9], [10, 11]]]) In [64]: a.T *= arange(3) --------------------------------------------------------------------------- exceptions.TypeError Traceback (most recent call last) /home/charris/workspace/microsat/tycho-work/<ipython console> TypeError: attribute 'T' of 'numpy.ndarray' objects is not writable In [65]: a Out[65]: array([[[ 0, 0], [ 0, 0]], [[ 4, 5], [ 6, 7]], [[16, 18], [20, 22]]]) Chuck

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Installation - Numpy 1.0.1 Suse Linux 10.1
by Gustavo Mercier Dec. 12, 2006

Dec. 12, 2006

Hi! I am trying to install Numpy 1.0.1 in a Linux Box (Suse Linux 10.1; gcc 4.1x). I have done this successfully with previous versions up to 1.0b5. However, I now run into problems. It compiles and installs ok, but upon opening python and importing numpy it hangs with an unresolved reference when loading linalg. The reference is to a _gfortran...function. I use Atlas and this was compiled with gfortran, and following the instructions to combine lapack with atlas. blas was also compiled the same way. I see that Numpy is being compiled with g77. I am trying to add the reference to the gfortran library object in /usr/lib but I don't see how to do this as an option in distutils. I also try to change the compiler from g77 to gfortran, but I failed. Setting --fcompiler anc --ccompiler options led to failures due to trying to compile c code with the fortran compiler. Any suggestions? Thanks for your help! -- Gustavo A. Mercier, Jr. MD,PhD gamercier(a)yahoo.com 469-396-6750 - cell

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numpy book
by Sven Schreiber Dec. 12, 2006

Dec. 12, 2006

Hi, now that 1.0 is out I would like to ask again about the status of the numpy ebook. Is there any mechanism in place where owners can get the updates? Note that this is not a request to Travis to send me the latest version by private email. That would be inefficient and my need is not that urgent. Nevertheless I think that issue should be settled. Excuse me if I've missed something obvious in that respect. Thanks, Sven ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642

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a==b for numpy arrays
by Abel Daniel Dec. 12, 2006

Dec. 12, 2006

> Hi! My unittests got broken because 'a==b' for numpy arrays returns an array instead of returning True or False: >>> import numpy >>> a = numpy.array([1, 2]) >>> b = numpy.array([1, 4]) >>> a==b array([True, False], dtype=bool) This means, for example: >>> if a==b: ... print 'equal' ... Traceback (most recent call last): File "<stdin>", line 1, in ? ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() >>> Now, I think that having a way of getting an element-wise comparison (i.e. getting an array of bools) is great. _But_ why make that the result of a '==' comparison? Is there any actual code that does, for example >>> result_array = a==b or any variant thereof? Thanks in advance, Daniel

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Numeric memory leak when building Numeric.array from numarray.array
by Alexandre Fayolle Dec. 11, 2006

Dec. 11, 2006

Hi, I'm facing a memory leak on an application that has to use numarray and Numeric (because of external dependencies). The problem occurs when building a Numeric array from a numarray array: import Numeric import numarray import sys atest = numarray.arange(200) temp = Numeric.array(atest) print sys.getrefcount(atest) # prints 3 print sys.getrefcount(temp) # prints 2 I'm running numarray 1.5.2 and Numeric 24.2 I can work around this by using an intermediate string representation: temp = Numeric.fromstring(atest.tostring(), atest.typecode()) temp.shape = atest.shape -- Alexandre Fayolle LOGILAB, Paris (France) Formations Python, Zope, Plone, Debian: http://www.logilab.fr/formations Développement logiciel sur mesure: http://www.logilab.fr/services Informatique scientifique: http://www.logilab.fr/science Reprise et maintenance de sites CPS: http://www.migration-cms.com/

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lapack_lite dgesv
by R. David Dec. 11, 2006

Dec. 11, 2006

Hello, I am trying to use the lapack_lite dgesv routine. The following sample code : from numpy import * [....] a=zeros((nbrows,nbcols),float,order='C') [....] ipiv=zeros((DIM),int,order='C') [....] linalg.lapack_lite.dgesv(DIM,1,a,DIM,asarray(ipiv),b,DIM,info) leads do the followin error message : lapack_lite.LapackError: Parameter ipiv is not of type PyArray_INT in lapack_lite.dgesv I don't understand the type problem for ipiv ! Indeed, the type of 'a' is OK, ipiv is created the same way than a, but something goes wrong. Do you have a clue for this ? Regards, Romaric -- -------------------------------------- R. David - david(a)icps.u-strasbg.fr Tel. : 03 90 24 45 48 (Fax 45 47) --------------------------------------

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ANN: An alternative to numpy.core.ma
by Pierre GM Dec. 10, 2006

Dec. 10, 2006

All, I just posted on the DeveloperZone of the wiki the latest version of maskedarray, an alternative to numpy.core.ma. You can download it here: http://projects.scipy.org/scipy/numpy/attachment/wiki/MaskedArray/maskedarr… The package has three modules: core (with the basic functions of numpy.core.ma), extras (which adds some functions, such as apply_along_axis, or the concatenator mr_), and testutils (which adds support for maskedarray for the tests functions). It also comes with its test suite (available in the tests subdirectory). For those of you who were not aware of it, the new MaskedArray is a subclass of ndarray, and it accepts any subclass of ndarray as data. You can use it as you would with numpy.core.ma.MaskedArray. For those of you who already tested the package, the main modifications are: - the reorganization of the initial module in core+extras. - Data are now shared by default (in other terms, the copy flag defaults to False in MaskedArray.__new__), for consistency with the rest of numpy. - An additional boolean flag has been introduced: keep_mask (with a default of True). This flag is useful when trying to mask a mask array: it tells __new__ whether to keep the initial mask (in that case, the new mask will be combined with the old mask) or not (in that case, the new mask replaces the old one). - Some functions/routines that were missing have been added (any/all...) As always, this is a work in progress. In particular, I should really check for the bottlenecks: would anybody have some pointers ? If you wanna be on the safe, optimized side, stick to numpy.core.ma. Otherwise, please try this new implementation, and don't forget to give me some feedback! PS: Technical question: how can I delete some files in the DeveloperZone wiki ? The maskedarray.py, test_maskedarray.py, test_subclasses.py are out of date, and should be replaced by the package. Thanks a lot in advance !

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griddata in python from x,y,z coordinates
by Morten Bjerkås Dec. 10, 2006

Dec. 10, 2006

Dear I wounder about if you know about a command in numpy similar to the command griddata in MATLAB. This command makes a grid out of a set of x,y,z coordinates. best regards Morten

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compile scipy by using intel compiler
by Gennan Chen Dec. 9, 2006

Dec. 9, 2006

Hi! All, I have a dual opteron 285 with 8G ram machine. And I ran FC6 x86_64 on that. I did manage to get numpy (from svn) compiled by using icc 9.1.0.45 and mkl 9.0 ( got 3 errors when I ran the est). But no such luck for scipy (from svn). Below is the error: Lib/special/cephes/mconf.h(137): remark #193: zero used for undefined preprocessing identifier #if WORDS_BIGENDIAN /* Defined in pyconfig.h */ ^ Lib/special/cephes/const.c(92): error: floating-point operation result is out of range double INFINITY = 1.0/0.0; /* 99e999; */ ^ Lib/special/cephes/const.c(97): error: floating-point operation result is out of range double NAN = 1.0/0.0 - 1.0/0.0; ^ Lib/special/cephes/const.c(97): error: floating-point operation result is out of range double NAN = 1.0/0.0 - 1.0/0.0; ^ compilation aborted for Lib/special/cephes/const.c (code 2) error: Command "icc -O2 -g -fomit-frame-pointer -mcpu=pentium4 -mtune=pentium4 -march=pentium4 -msse3 -axW -Wall -fPIC -c Lib/special/cephes/const.c -o build/temp.linux-x86_64-2.4/Lib/special/cephes/const.o" failed with exit status 2 Did anyone has a solution for this? BTW, the 3 error I got from numpy are: File "/usr/lib64/python2.4/site-packages/numpy/lib/tests/test_ufunclike.py", line 25, in test_ufunclike Failed example: nx.sign(a) Expected: array([ 1., -1., 0., 0., 1., -1.]) Got: array([ 1., -1., -1., 0., 1., -1.]) ********************************************************************** File "/usr/lib64/python2.4/site-packages/numpy/lib/tests/test_ufunclike.py", line 40, in test_ufunclike Failed example: nx.sign(a, y) Expected: array([True, True, False, False, True, True], dtype=bool) Got: array([True, True, True, False, True, True], dtype=bool) ********************************************************************** File "/usr/lib64/python2.4/site-packages/numpy/lib/tests/test_ufunclike.py", line 43, in test_ufunclike Failed example: y Expected: array([True, True, False, False, True, True], dtype=bool) Got: array([True, True, True, False, True, True], dtype=bool) Are these error serious?? Or maybe I should get back to gcc? Anyone got a good speed up by using icc and mkl? -- Gen-Nan Chen, PhD

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