Mailman 3 May 2014 - NumPy-Discussion

Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 92, Issue 19
by rodrigo koblitz 15 May '14

15 May '14

Dear Smith, that's exactly what I want. Thank! Dear Josef, I'm not thinking in publishing nothing with code. If you have some interesting I can show some codes. But it's probably very basic. Mainly I'm constructing some basics functions for model selection. R it's very good with this (bestglm, leaps...) and I see few things in python. Finaly, Have scipy discussion list yet? I'm not received nothing to months. abraços, Koblitz 2014-05-15 14:00 GMT-03:00 <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. smoothing function (rodrigo koblitz) > 2. Fancy Indexing of Structured Arrays is Slow (Dave Hirschfeld) > 3. [JOB] Scientific software engineer at the Met Office (Phil Elson) > 4. Re: smoothing function (josef.pktd(a)gmail.com) > 5. Re: smoothing function (Nathaniel Smith) > 6. Re: smoothing function (josef.pktd(a)gmail.com) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Thu, 15 May 2014 09:04:03 -0300 > From: rodrigo koblitz <rodrigokoblitz(a)gmail.com> > Subject: [Numpy-discussion] smoothing function > To: numpy-discussion(a)scipy.org > Message-ID: > < > CAAZkdU_5yw9qigWVofVrPZLptgs75q14Y7vaWoGpQW_nqtrpdA(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > Buenos, > I'm reading Zuur book (ecology models with R) and try make it entire in > python. > Have this function in R: > M4 <- gam(So ? s(De) + factor(ID), subset = I1) > > the 's' term indicated with So is modelled as a smoothing function of De > > I'm looking for something close to this in python. > > Someone can help me? > > abra?os, > Koblitz >

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smoothing function
by rodrigo koblitz 15 May '14

15 May '14

Buenos, I'm reading Zuur book (ecology models with R) and try make it entire in python. Have this function in R: M4 <- gam(So ∼ s(De) + factor(ID), subset = I1) the 's' term indicated with So is modelled as a smoothing function of De I'm looking for something close to this in python. Someone can help me? abraços, Koblitz

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[JOB] Scientific software engineer at the Met Office
by Phil Elson 15 May '14

15 May '14

I just wanted to let you know that there is currently a vacancy for a full-time developer at the Met Office, the UK's National Weather Service, within our Analysis, Visualisation and Data (AVD) team. I'm posting on this list as the Met Office's AVD team are heavily involved in the development of Python packages to support the work that our scientists undertake on a daily basis. The vast majority of the AVD team's time is spent working on our own open source Python packages Iris, cartopy and biggus as well as working on packages such as numpy, scipy, matplotlib and IPython; so we don't see this as just a great opportunity to work within a world class scientific organisation, but a role which will also deliver real benefits to the wider scientific Python community. Please see http://goo.gl/3ScFaZ for full details and how to apply, or contact HREnquiries(a)metoffice.gov.uk if you have any questions. Many Thanks, Phil

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numpy quad and maple
by Ihab Riad 10 May '14

10 May '14

Hi, I did the following integral with numpy quad quad(lambda h: np.exp(-.5*2334.0090702455204-1936.9610182100055*5*log10(h)-(12.5*2132.5498892927189)*log10(h)*log10(h)),0,inf) and I get the following values (1.8368139214123403e-126, 3.3631976081491865e-126). When I do the integral with maple and mathematica I get 2.643019766*10^(-127) I think the numpy value is not correct. Could any one please advice me on that. Cheers Ihab -- View this message in context: http://numpy-discussion.10968.n7.nabble.com/numpy-quad-and-maple-tp37559.ht… Sent from the Numpy-discussion mailing list archive at Nabble.com.

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segfault from scipy.io.netcdf with scipy-0.14 numpy-0.18
by Robert McGibbon 08 May '14

08 May '14

Hey all, The travis tests for a library I work on just stopped working, and I tracked down the bug to the following test case. The file "MDTraj/testing/reference/mdcrd.nc" is a netcdf3 file in our repository ( https://github.com/rmcgibbo/mdtraj/tree/master/MDTraj/testing/reference). this script: conda install --yes scipy==0.13 numpy==1.7 --quiet python -c 'import scipy.io; print scipy.io.netcdf.netcdf_file("MDTraj/testing/reference/mdcrd.nc").variables["coordinates"][:].sum()' conda install --yes scipy==0.14 numpy==1.8 --quiet python -c 'import scipy.io; print scipy.io.netcdf.netcdf_file("MDTraj/testing/reference/mdcrd.nc").variables["coordinates"][:].sum()' works on scipy==0.13 numpy==1.7, but segfaults on scipy==0.14 numpy==1.8. I got the segfault on both linux and osx. I tried compiling a new version of numpy from source with debug symbols using `python setup.py build_ext -g install`, but couldn't get a useful traceback. $ gdb --core=core (gdb) bt #0 0x00007fd4f7887b18 in ?? () #1 0x00007fd4f786ecc6 in ?? () #2 0x0000000000000000 in ?? () Anyone have any advice for tracking this down? -Robert

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Re: [Numpy-discussion] IDL vs Python parallel computing
by Siegfried Gonzi 08 May '14

08 May '14

On 08/05/2014 04:00, numpy-discussion-request(a)scipy.org wrote: > 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..." > > > > > > ------------------------------ > > Message: 2 > Date: Wed, 7 May 2014 19:25:32 +0100 > From: Nathaniel Smith <njs(a)pobox.com> > Subject: Re: [Numpy-discussion] IDL vs Python parallel computing > To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> > Message-ID: > <CAPJVwBnqMnrKo0=tTHLN1pvePWoV_rh+mJ_4PTwPDxb8d0Hq3Q(a)mail.gmail.com> > Content-Type: text/plain; charset=UTF-8 > > On Wed, May 7, 2014 at 7:11 PM, Sturla Molden <sturla.molden(a)gmail.com> wrote: > That said, reading data stored in text files is usually a CPU-bound > operation, and if someone wrote the code to make numpy's text file > readers multithreaded, and did so in a maintainable way, then we'd > probably accept the patch. The only reason this hasn't happened is > that no-one's done it. To add to the confusion what IDL offers: http://www.exelisvis.com/Support/HelpArticlesDetail/TabId/219/ArtMID/900/Ar… I am not using IDL (and was never interested in IDL at all as it is a horrible language) any more except for some legacy code. Nowadays I am mostly on Python. -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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IDL vs Python parallel computing
by Siegfried Gonzi 08 May '14

08 May '14

Hi all I noticed IDL uses at least 400% (4 processors or cores) out of the box for simple things like reading and processing files, calculating the mean etc. I have never seen this happening with numpy except for the linalgebra stuff (e.g lapack). Any comments? Thanks, Siegfried -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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Re: [Numpy-discussion] IDL vs Python parallel computing
by Siegfried Gonzi 07 May '14

07 May '14

On 08/05/2014 04:00, numpy-discussion-request(a)scipy.org wrote: > 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..." > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 07 May 2014 20:11:13 +0200 > From: Sturla Molden <sturla.molden(a)gmail.com> > Subject: Re: [Numpy-discussion] IDL vs Python parallel computing > To: numpy-discussion(a)scipy.org > Message-ID: <lkdt01$jrc$1(a)ger.gmane.org> > Content-Type: text/plain; charset=ISO-8859-1; format=flowed > > On 03/05/14 23:56, Siegfried Gonzi wrote: > > I noticed IDL uses at least 400% (4 processors or cores) out of the box > > for simple things like reading and processing files, calculating the > > mean etc. > > The DMA controller is working at its own pace, regardless of what the > CPU is doing. You cannot get data faster off the disk by burning the > CPU. If you are seeing 100 % CPU usage while doing file i/o there is > something very bad going on. If you did this to an i/o intensive server > it would go up in a ball of smoke... The purpose of high-performance > asynchronous i/o systems such as epoll, kqueue, IOCP is actually to keep > the CPU usage to a minimum. It is probbaly not so much about reading in files. But I just noticed (top command) it for simple things like processing say 4 dimensional fields (longitute, latitude, altitutde, time) and calculating column means or moment statistics over grid boxes and writing the fields out again and things like that. But it never uses more than 400%. I haven't done any thorough testing of where and why the 400% really kicks in and if IDL is cheating here or not. -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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Page on Windows DLLs and Python extension - please edit
by Matthew Brett 07 May '14

07 May '14

Hi, I'm compiling information on DLLs for Windows building, in the hope that it's helpful for deciding on where to go with these. Please do check and see whether this fits with your understanding - it can be hard to follow the docs on this stuff: https://github.com/numpy/numpy/wiki/windows-dll-notes Cheers, Mathew

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List of arrays failing index(), remove() etc
by mfm24 07 May '14

07 May '14

I'm having a problem I haven't seen elsewhere (and apologies if it has been answered before). I see the following behavior (copied verbatim from a python session): Python 2.7.4 (default, Apr 6 2013, 19:55:15) [MSC v.1500 64 bit (AMD64)] on win32Type "help", "copyright", "credits" or "license" for more information.>>> import numpy as np>>> x=[[np.zeros(10)] for i in range(10)]>>> x.index(x[0])0>>> x.index(x[1])Traceback (most recent call last): File "", line 1, in ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()>>> x[1].append(np.zeros(10))>>> x.index(x[1])1 Any ideas why I see a ValueError when trying to find the index of a list containing a single ndarray? -Matt -- View this message in context: http://numpy-discussion.10968.n7.nabble.com/List-of-arrays-failing-index-re… Sent from the Numpy-discussion mailing list archive at Nabble.com.

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