Mailman 3 January 2009 - NumPy-Discussion

record array with array elements
by Igor Sylvester Jan. 7, 2009

Jan. 7, 2009

Everyone, Shouldn't the itemsize below be 2? >>> import numpy as np >>> dtype = np.dtype( [ (((2,), 'top'), [('nested', 'i1')]) ] ) >>> dtype.itemsize 1 >>> np.__version__ '1.0.4' The elements of the dtype are of type array of size 2. Each element is a (nested) record array of size 2 with one field of type 'i1'. In contiguous memory, this should look identical to an 'i1' array of size 2. -Igor Sylvester

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Re : [Newbie] Fast plotting
by Jean-Baptiste Rudant Jan. 6, 2009

Jan. 6, 2009

Hello, I'm not an expert. Something exists in matplotlib, but it's not very efficient. import matplotlib.mlab import numpy N = 1000 X = numpy.random.randint(0, 10, N) Y = numpy.random.random(N) recXY = numpy.rec.fromarrays((X, Y), names='x, y') summary = matplotlib..mlab.rec_groupby(recXY, ('x',), (('y', numpy.mean, 'y_avg'),)) Jean-Baptiste Rudant ________________________________ De : Franck Pommereau <pommereau(a)univ-paris12.fr> À : Discussion of Numerical Python <numpy-discussion(a)scipy.org> Envoyé le : Mardi, 6 Janvier 2009, 10h35mn 01s Objet : [Numpy-discussion] [Newbie] Fast plotting Hi all, and happy new year! I'm new to NumPy and searching a way to compute from a set of points (x,y) the mean value of y values associated to each distinct x value. Each point corresponds to a measure in a benchmark (x = parameter, y = computation time) and I'd like to plot the graph of mean computation time wrt parameter values. (I know how to plot, but not how to compute mean values.) My points are stored as two arrays X, Y (same size). In pure Python, I'd do as follows: s = {} # sum of y values for each distinct x (as keys) n = {} # number of summed values (same keys) for x, y in zip(X, Y) : s[x] = s.get(x, 0.0) + y n[x] = n.get(x, 0) + 1 new_x = array(list(sorted(s))) new_y = array([s[x]/n[x] for x in sorted(s)]) Unfortunately, this code is much too slow because my arrays have millions of elements. But I'm pretty sure that NumPy offers a way to handle this more elegantly and much faster. As a bonus, I'd be happy if the solution would allow me to compute also standard deviation, min, max, etc. Thanks in advance for any help! Franck _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion

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Old-style classes in tests
by Tom Wright Jan. 6, 2009

Jan. 6, 2009

I am currently working on the Ironclad project porting numpy to Ironpython. It would be quite useful for me if HermitianTestCase in test_linalg.py was a new style-class instead of an old-style class - since Ironpython has a bug where dir operations do not work for classes inheriting from both old- and new- style classes and I'd very much prefer not to patch my version of numpy. In general, it would be useful if whenever this multiple inheritence pattern is used new-style classes are used rather than old style classes. This would require the following classes to change: test_numerictypes - create_values, read_values_plain, read_values_nested test_print - create_zeros, create_values, assign_values, byteorder_values test_io - Roundtriptest test_linalg - LinalgTestCase, HermitianTestCase Would people be ameniable to these change? Thank you very much for your help, Tom Wright

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Handling methods of object-arrays
by Stéfan van der Walt Jan. 6, 2009

Jan. 6, 2009

Hi all, What is the exact protocol for evaluating functions like "real" and "imag" on object arrays? For example, I'm looking at x = np.array([np.array(3+1j), np.array(4+1j)], dtype=object) For which both In [4]: x.real Out[4]: array([(3+1j), (4+1j)], dtype=object) and In [6]: np.real(x) Out[6]: array([(3+1j), (4+1j)], dtype=object) does nothing, so that I have to do In [8]: [np.real(e) for e in x] Out[8]: [array(3.0), array(4.0)] or [e.real for e in x]. Would it make sense make np.real aware of the above scenario? Regards Stéfan

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when will osx linker option -bundle be reflected in distutils
by Garry Willgoose Jan. 6, 2009

Jan. 6, 2009

> > I was just wondering what plans there were to reflect the different > > linker options (i.e. -bundle instead of -shared) that are required > on > > OSX in the fcompiler files within distutils. While its a minor thing > > it always catches the users of my software when they either install > > fresh or update numpy ... and sometimes on a bad day it even catches > > me ;-) > > I'm sorry; I don't follow. What problems are you having? -- > -- Robert Kern > ----------------------------------------------- OK for example the distribution g95.py in distutils/fcompiler has the following code executables = { 'version_cmd' : ["g95", "--version"], 'compiler_f77' : ["g95", "-ffixed-form"], 'compiler_fix' : ["g95", "-ffixed-form"], 'compiler_f90' : ["g95"], 'linker_so' : ["g95","-shared"], 'archiver' : ["ar", "-cr"], 'ranlib' : ["ranlib"] } For osx you need to modify it to executables = { 'version_cmd' : ["g95", "--version"], 'compiler_f77' : ["g95", "-ffixed-form"], 'compiler_fix' : ["g95", "-ffixed-form"], 'compiler_f90' : ["g95"], 'linker_so' : ["g95","-shared"], 'archiver' : ["ar", "-cr"], 'ranlib' : ["ranlib"] } import sys if sys.platform.lower() == 'darwin': executables[linker_so'] = ["g95","-Wall -bundle"] The 'shared' option is not implemented in the osx linker. Not sure what the underlying difference between 'shared' and 'bundle' is but this substitution is necessary and this has been working for me for the last year or so. You also need the -Wall but for reasons that completely escape me. The same goes for gfortran and intel (both of which I use) and I assume the other compilers that are available for OSX. ==================================================================== Prof Garry Willgoose, Australian Professorial Fellow in Environmental Engineering, Director, Centre for Climate Impact Management (C2IM), School of Engineering, The University of Newcastle, Callaghan, 2308 Australia. Centre webpage: www.c2im.org.au Phone: (International) +61 2 4921 6050 (Tues-Fri AM); +61 2 6545 9574 (Fri PM-Mon) FAX: (International) +61 2 4921 6991 (Uni); +61 2 6545 9574 (personal and Telluric) Env. Engg. Secretary: (International) +61 2 4921 6042 email: garry.willgoose(a)newcastle.edu.au; g.willgoose(a)telluricresearch.com email-for-life: garry.willgoose(a)alum.mit.edu personal webpage: www.telluricresearch.com/garry ==================================================================== "Do not go where the path may lead, go instead where there is no path and leave a trail" Ralph Waldo Emerson ====================================================================

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ANN: PyMC 2.0
by Christopher Fonnesbeck Jan. 6, 2009

Jan. 6, 2009

Numpy list members, It gives me great pleasure to be able to announce the long-awaited release of PyMC 2.0. Platform-specific installers have been uploaded to the Google Code page (Mac OSX) and the Python Package Index (all other platforms), along with the new user's guide (http://pymc.googlecode.com/files/UserGuide2.0.pdf ). PyMC is a python module that implements Bayesian statistical models and ﬁtting algorithms, including Markov chain Monte Carlo. Its ﬂexibility makes it applicable to a large suite of problems as well as easily extensible. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-ﬁt and convergence diagnostics. PyMC 2.0 is a quantum leap from the 1.3 release. It includes a completely revised object model and syntax, more efficient log- probability computation, a variety of specialised MCMC algorithms, and an expanded set of optimised probability distributions. As a result, models built for previous versions of PyMC will not run under version 2.0. I would like to particularly thank Anand Patil and David Huard, who have done most of the work on this version, and to all the users who have sent questions, comments and bug reports over the past year or two. Please keep the feedback coming! Please report any problems with the release to the issues page (http://code.google.com/p/pymc/issues/list ). Python Package Index: http://pypi.python.org/pypi/pymc/ Google Code: http://pymc.googelcode.com Mailing List: http://groups.google.com/group/pymc Happy new year, Chris -- Christopher J. Fonnesbeck Department of Mathematics and Statistics University of Otago, PO Box 56 Dunedin, New Zealand

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Zoom fft code
by Nadav Horesh Jan. 5, 2009

Jan. 5, 2009

I am looking for a zoom fft code. I found an old code by Paule Kinzle (a matlab code with a translation to numarray), but its 2D extension (czt1.py) looks buggy. Nadav.

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unique1d and asarray
by Pierre GM Jan. 5, 2009

Jan. 5, 2009

All, Currently, np.unique1d uses np.asarray to ensure the input is an array. The problem is that np.asarray transforms a MaskedArray into a regular ndarray, the missing information is lost and the result is not correct. If we used np.asanyarray instead, subclasses are recognized properly, the mask is recognized by argsort and the result correct. Is there a reason why we use np.asarray instead of np.asanyarray ? Thanks a lot in advance, P.

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Invitation to connect on LinkedIn
by Yunfeng Zhang Jan. 5, 2009

Jan. 5, 2009

LinkedIn ------------ Discussion, I'd like to add you to my professional network on LinkedIn. - Yunfeng Learn more: https://www.linkedin.com/e/isd/443568042/FmoYJeHZ/ ------------------------------------------ What is LinkedIn and why should you join? http://learn.linkedin.com/what-is-linkedin/ ------ (c) 2008, LinkedIn Corporation

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cobyla, f2py
by gh zhao Jan. 5, 2009

Jan. 5, 2009

just try f2py according to http://www.scipy.org/F2PY_Windows. The following woks in dos cmd window. E:\Miser3p2F\opcntrl\python E:\python25\scripts\f2py.py -c --fcompiler=gnu95 --compiler=mingw32 -lmsvcr71 -m cobyla cobyla.pyf cobyla2.f trstlp.f But I directly run run_compile() of f2py2e with sys.argv=sys.argv+['-c','--fcompiler=gnu95','--compiler=mingw32', '-lmsvcr71','-m', 'cobyla','E:\\Miser3p2F\opcntrl\\cobyla.pyf','E:\\Miser3p2F\opcntrl\\cobyla2.f','E:\\Miser3p2F\opcntrl\\trstlp.f'] , it does not work. I have tried several simple examples, they all work. I do not know why. ghzhao from Curtin ___________________________________________________________ 好玩贺卡等你发，邮箱贺卡全新上线！ http://card.mail.cn.yahoo.com/

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