Mailman 3 October 2006 - NumPy-Discussion

control
by Rootsraquo File 24 Oct '06

24 Oct '06

Namibia Nauru or Nepal or Antilles Caledonia Nicaragua Niger or Nigeria Niue Norfolk North Korea Norway Oman Pakistan Palau in Panama Papua or Paraguay Peru Pitcairn of. Uta Terranigma a Tetris Tokimeki Memorial lots classes studying choices say a Incredible agree is tunes selection. Netsasoft Arabic jun in Dangerous sep of apr Boby xeik Chinese Simplified sundoit Oohoo jul Alleykat John Lgoosens Stefan oct delosfarsi am aa Bullitt Galixte of de Ezcom sie German. Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium am Belize Benin. Now still might be searching special having or brilliant a idea what could perfect avatar? Js displays only error messages show css errors problem much appearing difficult looking. Dvdrw dl Dvdram bdr Bdre Dbre dl udf toma Undelete a! Path width of height altpowered border successor highly of popular opensource bulletin system or Rewritten ground flexible secure feature. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia of Falkland Malvinas Faroe Fiji Finland France French Polynesia a Southern Gabon is. Convertors generally or written maintained Group guarantee status no bad which occur risk provided enquires directed relevant of author is. Links unknowns or Photopaul National Opinion am Business Technology am Jobs is Real Estate Cars Dating classified in adsitemap Privacy Conditions. ------------------------------------------------------------------------- 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 _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

1 0

LPC, durbin-levinson, covariance, autocorrelation and lattice method?
by Tobias Bengtsson 24 Oct '06

24 Oct '06

Hi I wish to do some Linear Predictive Coding, using durbin-levinson, covariance, autocorrelation or lattice method algoritms. However I don't know anything on digital signal processing, neither am I a star at math. I've ported http://www.phon.ucl.ac.uk/courses/spsci/dsp/lpc.html to python, but I couldn't get it to work, that was ported from fortran. Does scipy/numpy contain the means to help me? please reply directly to me as I'm not on the list. kind regards, Tobias ------------------------------------------------------------------------- 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 _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

2 1

Random integers
by Albert Strasheim 23 Oct '06

23 Oct '06

Hello all I'm trying to generate random 32-bit integers. None of the following seem to do the trick with NumPy 1.0.dev3383: In [32]: N.random.randint(-2**31, 2**31-1) ValueError: low >= high In [43]: N.random.random_integers(-2**31, 2**31-1) OverflowError: long int too large to convert to int In [45]: N.random.randint(-2**31, 2**31-1) ValueError: low >= high Am I missing something obvious? Cheers, Albert ------------------------------------------------------------------------- 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

4 3

Portland cement
by This will 23 Oct '06

23 Oct '06

Although should is be moist no am standing of surface when is applied. Account Help Store Locator a Contact us Door is Locks Screws Safety Closet Storage More in Measuring is Tools Power Handpower is Fans or Light of Fixtures Telephones Wiring Devices. Mix tightly area sure fill areas mixture begins set down a either a metal trowel wooden float of finish rough wood is float finishing job of completed? Adding grout Moistening a prevents it a from drawing which dry out mixture Although should be moist no standing surface when is of applied After has been moistened cleaned apply putty knife a? Done chipped a around of stiff edges created chiseling provides good a youll apply a sidewalks have prime thin creamy am some cases? Cans liquid smaller inside am can Pour paste layers layer partially applying am. Driveways Holes Walls Walks Broken Corners can grout made of a Portland am cement water add just of enough water or form is thick paste Moisten am old along crack for several hours. Housewares Search by keyword or item am Answers ace Faqs Project Howtos a Solution am. Prime thin creamy some cases simply filling important desirable only readymix. Nor retailer or held damages injuries resulting is document copy Copyright Careersace Club in Helpdesk Privacy Policy Affiliate product page. ------------------------------------------------------------------------- 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 _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

1 0

slicing suggestion
by JJ 23 Oct '06

23 Oct '06

Hello. I have a suggestion that might make slicing using matrices more user-friendly. I often have a matrix of row or column numbers that I wish to use as a slice. If K was a matrix of row numbers (nx1) and M was a nxm matrix, then I would use ans = M[K.A.ravel(),:] to obtain the matrix I want. It turns out that I use .A.ravel() quite a lot in my code, as I usually work with matrices rather than arrays. My suggestion is to create a new attribute, such as .AR, so that the following could be used: M[K.AR,:]. I believe this would be more concise, easier to read, and well used. If slices are made in both directions of the matrix, then the .A.ravel() becomes even more unwieldy. Does anyone else like this idea? John __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com ------------------------------------------------------------------------- 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

3 4

Strange results when sorting array with fields
by Albert Strasheim 23 Oct '06

23 Oct '06

Hello all I'm trying to sort an array with two fields, but I'm getting a result that doesn't seem to make sense. What I tried (first attempt): I have two 2-D arrays. I would like to sort one based on the sort of the other. I managed to do this with argsort. However, the fancy indexing required to get the sorted array using what argsort returned was very slow. I followed this example: http://www.scipy.org/Numpy_Example_List#head-9f8656795227e3c43e849c6c0435eee b32afd722 What I tried (second attempt): I created an array with two fields. I think/hope/expected that sorting the array would sort it on the first field in the dtype and then on the second. This is *much* faster than the fancy indexing approach. Code: import numpy as N print N.__version__ print dt = N.dtype([('aaa', N.float64), ('bbb', N.uint8)]) x = N.empty((2, 2), dt) xa = x[dt.names[0]] xa[:] = [[1.,2], [3.,4.]] xb = x[dt.names[1]] xb[:] = [[1, 2], [3, 4]] print 'before sort:' print x print x.sort(kind='quicksort') # other kinds not supported print 'after sort:' print x Output on my system: 1.0.dev3376 before sort: [[(1.0, 1) (2.0, 2)] [(3.0, 3) (4.0, 4)]] after sort: [[(2.0, 2) (1.0, 1)] [(4.0, 4) (3.0, 3)]] The already sorted array has been unsorted in some way. Any thoughts? Thanks! Regards, Albert ------------------------------------------------------------------------- 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

3 4

Re: Model and experiment fitting
by Tom Loredo 23 Oct '06

23 Oct '06

Hi Sebastian, I'm still unclear about the problem. From your last description, it sounds like the problem is *not* 2-D; you are trying to model a 1-d function of a 1-D argument (unless B is not the scalar field strength, but rather \vec B, the 3-D field vector). Also, your problem appears to be a regression (i.e., curve/surface fitting) problem, not a density estimation problem (comparing samples drawn from two distributions), so the Numerical Recipes suggestion (which addresses comparing densities) is probably not relevant. It sounds like you have some observed data that perhaps can be modeled as y_i = f(x_i; theta) + e_i where e_i represent measurement error, f(x;theta) is the "true model" that gives the intensity as a function of the field strength, which depends on some parameters theta, and y_i are the measured intensities. I presume you know something about the y_i measurement errors (like a std error for all or for each of them). You also have a computational model that gives you simulation data that can be modeled as Y_i = g(X_i; theta) with (presumably) no Y_i "measurement error" (though perhaps there is an equivalent if you use Monte Carlo in the calculation or have other quantifiable sources of error). As you phrased the problem, it appears you know theta exactly for both the observational and simulation data (an unusual situation!). You just want to ascertain whether g is a good approximation to f. Is this correct? There is a large literature on problems like this where theta is *unknown* and one wants to either calibrate the simulation or infer theta for the observations from a sparse set of runs of the simulation at various theta. But no papers come immediately to mind for the known theta case, though the "validation" stage of some of the available papers address it. For the unknown theta case, the relevant literature is fairly new and goes under various names (DACE: Design & Analysis of Computer Experiments; BACCO: Bayesian Analysis of Computer Code Output; MUCM: Managing Uncertainty in Complex Models). The main tools are interpolators that *quantify uncertainty* in the interpolation and machinery to propagate uncertainty through subsequent analyses. Gaussian processes (which include things like splines, kriging and random walks as special cases, but with "error propagation") are used to build an "emulator" for the simulation (an emulator is an interpolator that also gives you measures of uncertainty in the interpolation). There are free codes available for Matlab and R implementing this paradigm, but it's a tricky business (as I am slowly discovering, having just started to delve into it). It is unclear from your description why the X_i cannot be made equal to the x_i. If either or both are uncontrollable---esp. if you cannot set x_i but have to rely on "nature" providing you x_i values that would differ from one set of observations to the next---this adds a layer of complexity that is not trivial. It becomes a "measurement error problem" (aka "errors-in-the-variables problem"), with subtle aspects (you can easily go astray by simply ignoring measurement errors on the x's; they do *not* "average out" as you get more sample points). This can (and has) been incorporated into the emulator framework, though I don't know if any of the free code does this. Part of the MUCM/BACCO approach is estimation of a "bias" or "misfit" term between the simulation and calibration data (here it would be delta(x) = g(x)-f(x)). Perhaps your problem can be phrased in terms of whether the bias function is significantly nonzero anywhere, and if so, where. There are various places to go to learn about this stuff if it interests you; here are a few links. The first two are to the home pages of two working groups at SAMSI (an interdisciplinary statistics/appl. math institute), which is devoting this year to research on methods and applications in analysis of computer models. http://www.stat.duke.edu/~fei/samsi/index.html http://www4.stat.ncsu.edu/~gawhite/SAMSIweb/ http://mucm.group.shef.ac.uk/ http://cran.r-project.org/src/contrib/Descriptions/BACCO.html http://www2.imm.dtu.dk/~hbn/dace/ A particularly accessible paper is by the Los Alamos statistics group: http://portal.acm.org/citation.cfm?id=1039891.1039922 Again, I don't think this literature directly addresses your problem, but it may provide the "right" way to approach it, if you are willing to do some work to connect your problem to this framework. The short answer to your question is that I think you will be hard pressed to get a good answer to your question using off-the-shelf SciPy tools. Good luck, Tom Loredo ------------------------------------------------- This mail sent through IMP: http://horde.org/imp/ ------------------------------------------------------------------------- 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

1 0

combinatorics
by Mathew Yeates 23 Oct '06

23 Oct '06

Hi Is there any support for combinatorics in numpy or scipy? Actually, all I need is to evaluate is (n/m) i.e. n choose m. If numpy doesn't have it, anyone know of a good public domain prog? Mathew ------------------------------------------------------------------------- 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

2 2

[Fwd: numpy error]
by Colin J. Williams 23 Oct '06

23 Oct '06

-------- Original Message -------- Subject: numpy error Date: Mon, 23 Oct 2006 20:01:33 +0200 From: Juergen Kareta <kareta(a)web.de> Newsgroups: gmane.comp.python.general Hello, this is my first try to get wxmpl-1.2.8 running. Therefor I installed: python 2.5 matplotlib-0.87.6.win32-py2.5.exe numpy-1.0rc3.win32-py2.5.exe on WinXP SP2 The result is a version mismatch (see below). Numpy version 1000002 seems to be numpy-1.0b5 which is not downloadable anymore. Any hints ? Thanks in advance. Jürgen traceback: from pylab import * RuntimeError: module compiled against version 1000002 of C-API but this version of numpy is 1000009 The import of the numpy version of the nxutils module, _nsnxutils, failed. This is is either because numpy was unavailable when matplotlib was compiled, because a dependency of _nsnxutils could not be satisfied, or because the build flag for this module was turned off in setup.py. If it appears that _nsnxutils was not built, make sure you have a working copy of numpy and then re-install matplotlib. Otherwise, the following traceback gives more details: Traceback (most recent call last): File "<interactive input>", line 1, in <module> File "C:\Python25\Lib\site-packages\pylab.py", line 1, in <module> from matplotlib.pylab import * File "C:\Python25\Lib\site-packages\matplotlib\pylab.py", line 199, in <module> import mlab #so I can override hist, psd, etc... File "C:\Python25\Lib\site-packages\matplotlib\mlab.py", line 64, in <module> import nxutils File "C:\Python25\Lib\site-packages\matplotlib\nxutils.py", line 17, in <module> from matplotlib._ns_nxutils import * ImportError: numpy.core.multiarray failed to import -- http://mail.python.org/mailman/listinfo/python-list ------------------------------------------------------------------------- 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

1 0

ix_ problem changing input arrays?
by Scott Ransom 23 Oct '06

23 Oct '06

I believe that ix_() has recently begun modifying the shapes of its input arrays. For instance: Python 2.4.4c0 (#2, Jul 30 2006, 18:20:12) [GCC 4.1.2 20060715 (prerelease) (Debian 4.1.1-9)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as N >>> a = N.array([1,2,3]) >>> b = N.array([7,6,5,4]) >>> ax, bx = N.ix_(a, b) >>> a array([[1], [2], [3]]) >>> b array([[7, 6, 5, 4]]) >>> N.__version__ '1.0.dev3379' Is this intended behaviour? 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 ------------------------------------------------------------------------- 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

2 1