Mailman 3 August 2013 - NumPy-Discussion

Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 83, Issue 35
by rodrigo koblitz Aug. 21, 2013

Aug. 21, 2013

Thanks Warren, ok, I will pay more atention in the next time and address correctly. abs, Koblitz

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Adding an axis argument to numpy.unique
by Joe Kington Aug. 21, 2013

Aug. 21, 2013

Hi everyone, I've recently put together a pull request that adds an `axis` kwarg to `numpy.unique` so that `unique`can easily be used to find unique rows/columns/sub-arrays/etc of a larger array. https://github.com/numpy/numpy/pull/3584 Currently, this works as a warpper around `unique`. If `axis` is specified, it reshapes the input to a 2D contiguous array, views each row as a single item, then passes it on to `unique`. For int and string dtypes, each row is viewed as a void dtype and therefore bitwise-equality is used for comparisons. For all other dtypes, the each row is viewed as a structured array. The current implementation has two main drawbacks: 1. For anything other than ints and strings, it's relatively slow. 2. It doesn't work with object arrays of any sort. I'd appreciate any thoughts/feedback folks might have on both the general idea and this specific implementation. It think it's a worthwhile addition, but I'm biased. Thanks! -Joe

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Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 83, Issue 33
by rodrigo koblitz Aug. 21, 2013

Aug. 21, 2013

Hi, How I can do this: int(scipy.comb(20314,117)) ... OverflowError: cannot convert float infinity to integer abs, Koblitz 2013/8/20 <numpy-discussion-request(a)scipy.org> > Send NumPy-Discussion mailing list submissions to > numpy-discussion(a)scipy.org > > To subscribe or unsubscribe via the World Wide Web, visit > http://mail.scipy.org/mailman/listinfo/numpy-discussion > or, via email, send a message with subject or body 'help' to > numpy-discussion-request(a)scipy.org > > You can reach the person managing the list at > numpy-discussion-owner(a)scipy.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of NumPy-Discussion digest..." > > > Today's Topics: > > 1. OS X binaries for releases (Ralf Gommers) > 2. Re: OS X binaries for releases (David Cournapeau) > 3. Re: OS X binaries for releases (KACVINSKY Tom) > 4. Re: OS X binaries for releases (David Cournapeau) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Tue, 20 Aug 2013 22:48:51 +0200 > From: Ralf Gommers <ralf.gommers(a)gmail.com> > Subject: [Numpy-discussion] OS X binaries for releases > To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> > Message-ID: > < > CABL7CQjaCXp2GrtT8HVmaYAjRm0xmtn1Qt71WKdnbGq7dLU0cQ(a)mail.gmail.com> > Content-Type: text/plain; charset="iso-8859-1" > > Hi all, > > Building binaries for releases is currently quite complex and > time-consuming. For OS X we need two different machines, because we still > provide binaries for OS X 10.5 and PPC machines. I propose to not do this > anymore. It doesn't mean we completely drop support for 10.5 and PPC, just > that we don't produce binaries. PPC was phased out in 2006 and OS X 10.6 > came out in 2009, so there can't be a lot of demand for it (and the > download stats at > http://sourceforge.net/projects/numpy/files/NumPy/1.7.1/confirm this). > > Furthermore I propose to not provide 2.6 binaries anymore. Downloads of 2.6 > OS X binaries were <5% of the 2.7 ones. We did the same with 2.4 for a long > time - support it but no binaries. > > So what we'd have left at the moment is only the 64-bit/32-bit universal > binary for 10.6 and up. What we finally need to add is 3.x OS X binaries. > We can make an attempt to build these on 10.8 - since we have access to a > hosted 10.8 Mac Mini it would allow all devs to easily do a release > (leaving aside the Windows issue). If anyone has tried the 10.6 SDK on 10.8 > and knows if it actually works, that would be helpful. > > Any concerns, objections? > > Cheers, > Ralf > > P.S. the same proposal applies of course also to scipy >

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Deprecation of financial routines
by Juan Luis Cano Aug. 20, 2013

Aug. 20, 2013

As now master is open for 1.9, following the discussion opened here https://github.com/numpy/numpy/issues/2880 it was suggested that we deprecate and eventually remove the financial functions in NumPy, because they pollute the main namespace and some are unimplemented. We could put them in a separate package, in case it doesn't exist yet. Nathaniel Smith and Ralf Gommers already gave +1, and Charles Harris suggested bringing this up in the mailing list. Thoughts?

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At.: question about RAM problem in numpy array code
by Josè Luis Mietta Aug. 19, 2013

Aug. 19, 2013

Hi experts! I have a core i3 3GB RAM Toshiba laptop. Im a newby Ubuntu 13.04, python and sage user. I note that RAM memory becomes full while running script (starts in 50% of RAM ocupation and becomes to 100% (full)). This generate that operating system become slower... In the code few numpy arrays are gereated (each one with ~700 elements or more). The script looks like this: OUTPUT=[] number of elements=[n1,n2,.....] numbers of executions =N forj innumber of elements: lalala=[] fori insrange(N): Algorithmisexecuted 'N'times an append one value inarray 'lalala'each time.Ineach execution three numpy matrix of 300x 300(=90000)elements (each one)participate.Thismatrix are called 'M','M_v'and'M_h'andare re-generated foreach 'i'index inthe for-cycle. Doingmath on all 'N'elements in'lalala'a 'pipipi'element isgenerated an thenappend intoOUTPUT array. When execution ends the OUTPUT array have the same lenght that 'number of elements' array. In each 'for' cycle, other arrayrs participate. What can I do for throubleshooting that? IS possible that the algorithm and old arrays are saving in RAM memory? If tahts possible, how can I di for deleting that? Waiting for your answers. Thanks a lot!

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Removal of numarray and oldnumeric modules.
by Charles R Harris Aug. 19, 2013

Aug. 19, 2013

Hi All, I've put up a PR <https://github.com/numpy/numpy/pull/3638> that removes the numarray and oldnumeric modules from Numpy 1.9. The hope is that 1.9 will be released in five to six months. If you have trouble with removal on that schedule, now is the time to complain. Chuck

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maintenance/1.8.x has been branched.
by Charles R Harris Aug. 19, 2013

Aug. 19, 2013

Master is open for 1.9. Chuck

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strange behavior of variable
by Sudheer Joseph Aug. 18, 2013

Aug. 18, 2013

Hi, I have defined a small function to find the n maximum values of an array as below. With in it I assign the input array to a second array and temporarily make the array location after first iteration as nan. I expected this temporary change to be limited to the second variable. However my initial variable gets modified. Can any one through some light to what is happening here?. In case of matlab this logic works. ###### #FUNCTION maxn ###### import numpy as np def max_n(a,n): b=a result=[] for i in np.arange(1,n+1): mxidx=np.where(b==max(b)) result.append(mxidx) b[mxidx]=np.nan result=np.ravel(result) return(result) ### TEST In [8]: x=np.arange(float(0),10) In [9]: max max max_n In [9]: max_n(x,2) Out[9]: array([9, 8]) In [10]: x Out[10]: array([ 0., 1., 2., 3., 4., 5., 6., 7., nan, nan]) *************************************************************** Sudheer Joseph Indian National Centre for Ocean Information Services Ministry of Earth Sciences, Govt. of India POST BOX NO: 21, IDA Jeedeemetla P.O. Via Pragathi Nagar,Kukatpally, Hyderabad; Pin:5000 55 Tel:+91-40-23886047(O),Fax:+91-40-23895011(O), Tel:+91-40-23044600(R),Tel:+91-40-9440832534(Mobile) E-mail:sjo.India@gmail.com;sudheer.joseph@yahoo.com Web- http://oppamthadathil.tripod.com ***************************************************************

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Upcoming 1.8 release.
by Charles R Harris Aug. 17, 2013

Aug. 17, 2013

Hi All, I'm thinking of making the 1.8.x branch next Sunday. Any complaints, thoughts? Chuck

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Test cases for integer behvaiour
by Arink Verma Aug. 17, 2013

Aug. 17, 2013

Hi All, For scalar operations Numpy first try to extract the underlying C value from a Python Integers. It causes bottleneck because it first converts the Python scalar into its matching NumPy scalar (e.g. PyLong -> int32) and then it extracts the C value from the NumPy scalar. Its quicker to just extract the value directly from the Python scalar. Raul did it for float but for integers case is different as it get handled by different OS differently. There are basically two standards for long on 64 bit os, Microsoft uses long = int (32 bits), linux uses long = long long (64 bits). Hence, before getting into above mentioned modifications, there is need to have much test case to ensure behaviour of integer remains same. I have written fews at pr #3592 <https://github.com/numpy/numpy/pull/3592> - Arink

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