Mailman 3 November 2013 - NumPy-Discussion

numpy datetime64 NaT string conversion bug & patch
by Charles G. Waldman Nov. 28, 2013

Nov. 28, 2013

If you convert an array of strings to datetime64s and 'NaT' (or one of its variants) appears in the string, all subsequent values are rendered as NaT: (this is in 1.7.1 but the problem is present in current dev version as well) >>> import numpy as np >>> a = np.array(['2010', 'nat', '2030']) >>> a.astype(np.datetime64) array(['2010', 'NaT', 'NaT'], dtype='datetime64[Y]') The fix is to re-initalize 'dt' inside the loop in _strided_to_strided_string_to_datetime (patch attached) Correct behavior (with patch): >>> import numpy as np >>> a=np.array(['2010', 'nat', '2020']) >>> a.astype(np.datetime64) array(['2010', 'NaT', '2020'], dtype='datetime64[Y]') >>>

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diff and cumsum arguments
by josef.pktd＠gmail.com Nov. 28, 2013

Nov. 28, 2013

np.diff has keyword for `n-th order discrete difference` but cumsum has different arguments and cannot integrate n times What's the best "inverse function" for np.diff with n>1? I briefly tried polynomial, but without success so far. need two starting values to integrate for n=2 >>> a = np.random.randn(10) >>> a - np.cumsum(np.r_[a[0], np.cumsum(np.r_[a[1]-a[0], np.diff(a, n=2)])]) array([ 0.00000000e+00, 0.00000000e+00, 1.11022302e-16, 2.22044605e-16, 3.33066907e-16, 2.22044605e-16, 1.11022302e-16, 2.77555756e-16, 5.55111512e-16, 8.88178420e-16]) >>> a - np.concatenate((a[:2], np.cumsum(np.cumsum(np.diff(a, n=2))) + a[0] + np.arange(2, len(a)) * (a[1] - a[0]))) array([ 0.00000000e+00, 0.00000000e+00, 1.11022302e-16, 2.22044605e-16, 3.33066907e-16, 2.22044605e-16, 1.11022302e-16, 2.77555756e-16, 5.55111512e-16, 1.77635684e-15]) Josef

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(no subject)
by Matthew Brett Nov. 27, 2013

Nov. 27, 2013

Hi, I'm sorry if I missed something obvious - but is there a vectorized way to look for None in an array? In [3]: a = np.array([1, 1]) In [4]: a == object() Out[4]: array([False, False], dtype=bool) In [6]: a == None Out[6]: False (same for object arrays), Thanks a lot, Matthew

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xkcd on git commit messages.
by Charles R Harris Nov. 27, 2013

Nov. 27, 2013

Here <http://xkcd.com/>. Chuck

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Getting masked array boundary indices
by Sudheer Joseph Nov. 27, 2013

Nov. 27, 2013

Hi, I have a numpy array which is masked ( bathymetry), as seen below [ True] [ True] [ True] [ True]], fill_value = -9999.0) In [10]: depth[:,1130:1131] I need to find the indices where land(mask) is there along the boundaries and where water(value) is there along the boundaries, the above listing is along eastern boundary. Please help if there is a way to get starting and ending index of mask. I tried np.where but it gives another array as there are several mask points are there, I need to use some thing like "if neighbouring points are True and False then" index =i, but I am not getting the pythonic way to get this done. with best regards, Sudheer *************************************************************** 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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PyArray_BASE equivalent in python
by Peter Rennert Nov. 27, 2013

Nov. 27, 2013

Hi, I as the title says, I am looking for a way to set in python the base of an ndarray to an object. Use case is porting qimage2ndarray to PySide where I want to do something like: In [1]: from PySide import QtGui In [2]: image = QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg') In [3]: import numpy as np In [4]: a = np.frombuffer(image.bits()) --> I would like to do something like: In [5]: a.base = image --> to avoid situations such as: In [6]: del image In [7]: a Segmentation fault (core dumped) The current implementation of qimage2ndarray uses a C function to do PyArray_BASE(sipRes) = image; Py_INCREF(image); But I want to avoid having to install compilers, headers etc on target machines of my code just for these two lines of code. Thanks, P

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RFC: is it worth giving a lightning talk at PyCon 2014 on numpy vbench-marking?
by Yaroslav Halchenko Nov. 26, 2013

Nov. 26, 2013

Hi Guys, PyCon 2014 will be just around the corner from where I am, so I decided to attend. Being lazy (or busy) I haven't submitted any big talk but thinking to submit few lightning talks (just 5 min and 400 characters abstract limit), and I think it might be worth letting people know about my little project. I would really appreciate your sincere feedback (e.g. "not worth it" would be valuable too). Here is the title/abstract numpy-vbench -- speed benchmarks for NumPy http://yarikoptic.github.io/numpy-vbench provides collection of speed performance benchmarks for NumPy. Benchmarking of multiple maintenance and current development branches allows not only to timely react to new performance regressions, but also to compare NumPy performance across releases. Your contributions would help to guarantee that your code does not become slower with a new NumPy release. btw -- fresh results are here http://yarikoptic.github.io/numpy-vbench/ . I have tuned benchmarking so it now reflects the best performance across multiple executions of the whole battery, thus eliminating spurious variance if estimate is provided from a single point in time. Eventually I expect many of those curves to become even "cleaner". -- Yaroslav O. Halchenko, Ph.D. http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org Senior Research Associate, Psychological and Brain Sciences Dept. Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419 WWW: http://www.linkedin.com/in/yarik

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UNSUBSCRIBE Re: MKL + CPU, GPU + cuBLAS comparison
by regikeyz . Nov. 26, 2013

Nov. 26, 2013

UNSUBSCRIBE On 26 November 2013 13:37, Frédéric Bastien <nouiz(a)nouiz.org> wrote: > We have such benchmark in Theano: > > https://github.com/Theano/Theano/blob/master/theano/misc/check_blas.py#L177 > > HTH > > Fred > > On Tue, Nov 26, 2013 at 7:10 AM, Dinesh Vadhia > <dineshbvadhia(a)hotmail.com> wrote: > > Jerome, Thanks for the swift response and tests. Crikey, that is a > > significant difference at first glance. Would it be possible to compare > a > > BLAS computation eg. matrix-vector or matrix-matrix calculation? Thx! > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion(a)scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >

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Re: [Numpy-discussion] MKL + CPU, GPU + cuBLAS comparison
by Dinesh Vadhia Nov. 26, 2013

Nov. 26, 2013

Jerome, Thanks for the swift response and tests. Crikey, that is a significant difference at first glance. Would it be possible to compare a BLAS computation eg. matrix-vector or matrix-matrix calculation? Thx!

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MKL + CPU, GPU + cuBLAS comparison
by Dinesh Vadhia Nov. 26, 2013

Nov. 26, 2013

Probably a loaded question but is there a significant performance difference between using MKL (or OpenBLAS) on multi-core cpu's and cuBLAS on gpu's. Does anyone have recent experience or link to an independent benchmark?

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