Mailman 3 December 2012 - NumPy-Discussion

IPython receives $1.15 million from Alfred P. Sloan Foundation
by Jason Grout Dec. 12, 2012

Dec. 12, 2012

Hi everyone, Just FYI, IPython just received $1.15 million in funding from the Alfred P. Sloan Foundation to support development over the next 2 years. Fernando talks more about this in his post to the IPython mailing list: http://mail.scipy.org/pipermail/ipython-dev/2012-December/010799.html It's great to see a significant open-source python project that many of us use on a day-to-day basis get such great funding! Thanks, Jason -- Jason Grout

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unsubscribe
by ioannis syntychakis Dec. 11, 2012

Dec. 11, 2012

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segfaulting numpy with dot and datetime
by josef.pktd＠gmail.com Dec. 11, 2012

Dec. 11, 2012

>>> np.__version__ '1.6.2' >>> aa array([1970-01-13 96:00:00, 1970-01-13 120:00:00, 1970-01-13 144:00:00, 1970-01-13 168:00:00, 1970-01-13 192:00:00], dtype=datetime64[ns]) >>> np.dot(aa, [1]) <segfault> reported at http://stackoverflow.com/questions/13786209/regression-on-stock-data-using-… discussion https://groups.google.com/d/topic/pystatsmodels/5zpWBzSH8UE/discussion using linalg is "safe" but I doubt this casting to float is the desired result >>> np.linalg.pinv(aa[:,None]) array([[ 1.87768878e-19, 1.87784114e-19, 1.87799350e-19, 1.87814586e-19, 1.87829822e-19]]) >>> np.linalg.pinv(np.asarray(aa, float)[:,None]) array([[ 1.87768878e-19, 1.87784114e-19, 1.87799350e-19, 1.87814586e-19, 1.87829822e-19]]) Josef

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np.dstack vs np.concatenate?
by Chao YUE Dec. 10, 2012

Dec. 10, 2012

Dear all, I want to concate 13 mXn arrays into mXnX13 array. np version 1.6.2 I know the correct way to do that like here: http://stackoverflow.com/questions/8898471/concatenate-two-numpy-arrays-in-… yet the np.dstack documentation also gives something like this: Equivalent to ``np.concatenate(tup, axis=2)``. so I tried this: In [10]: a = np.arange(8).reshape(2,4) In [11]: b=np.arange(9,17).reshape(2,4) In [12]: abd = np.dstack((a,b)) In [13]: abd.shape Out[13]: (2, 4, 2) In [14]: np.testing.assert_array_equal(abd[...,0],a) In [15]: np.testing.assert_array_equal(abd[...,1],b) In [16]: np.concatenate((a,b),axis=2) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-16-ed1e1b2f1436> in <module>() ----> 1 np.concatenate((a,b),axis=2) ValueError: bad axis1 argument to swapaxes so I guess for the 13 arrays, np.dstack will work. I can also do something like: array_list_old = [arr1, arr2, arr3, arr4, arr5, arr6] array_list = [arr[...,np.newaxis] for arr in array_list_old] array = np.concatenate(tuple(array_list),axis=2) So is there some inconsistency in the documentation? thanks, Chao -- *********************************************************************************** Chao YUE Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL) UMR 1572 CEA-CNRS-UVSQ Batiment 712 - Pe 119 91191 GIF Sur YVETTE Cedex Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16 ************************************************************************************

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ImportError: libatlas.so.3: cannot open shared object file
by Allan Kamau Dec. 10, 2012

Dec. 10, 2012

I have built and installed numpy on Debian from source successfully as follows. export LAPACK=/usr/lib/lapack/liblapack.so;export ATLAS=/usr/lib/atlas-base/libatlas.so; python setup.py build; python setup.py install; Then I change directory from the numpy sources directory. Then I give the command "python -c 'import numpy; numpy.test()' I get the error pasted below. How can I resource this issue? Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/allank/SmashCell/local/lib/python2.7/site-packages/numpy/__init__.py", line 130, in <module> import add_newdocs File "/home/allank/SmashCell/local/lib/python2.7/site-packages/numpy/add_newdocs.py", line 9, in <module> from lib import add_newdoc File "/home/allank/SmashCell/local/lib/python2.7/site-packages/numpy/lib/__init__.py", line 13, in <module> from polynomial import * File "/home/allank/SmashCell/local/lib/python2.7/site-packages/numpy/lib/polynomial.py", line 18, in <module> from numpy.linalg import eigvals, lstsq File "/home/allank/SmashCell/local/lib/python2.7/site-packages/numpy/linalg/__init__.py", line 47, in <module> from linalg import * File "/home/allank/SmashCell/local/lib/python2.7/site-packages/numpy/linalg/linalg.py", line 22, in <module> from numpy.linalg import lapack_lite ImportError: libatlas.so.3: cannot open shared object file: No such file or directory

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pandas dataframe memory layout
by Daniel Wu Dec. 8, 2012

Dec. 8, 2012

For numpy array, we can choose to use either C style or Forran stype. For dataframe in Pandas, is it possible to choose memory layout as in numpy array?

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Numpy Trac migration
by Pauli Virtanen Dec. 8, 2012

Dec. 8, 2012

Hi, For those whom it may concern: Since the Numpy Trac -> Github migration is complete, I went ahead and added redirects projects.scipy.org/numpy/register -> github.com/numpy/numpy/issues projects.scipy.org/numpy/newticket -> github.com/numpy/numpy/issues plus an ugly warning bar on top to direct potential users towards Github. Cheers, -- Pauli Virtanen

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Hierarchical vs non-hierarchical ndarray.base and __array_interface__
by Gamblin, Todd Dec. 8, 2012

Dec. 8, 2012

Hi all, I posted on the change in semantics of ndarray.base here: https://github.com/numpy/numpy/commit/6c0ad59#commitcomment-2153047 And some folks asked me to post my question to the numpy mailing list. I've implemented a tool for mapping processes in parallel applications to nodes in cartesian networks. It uses hierarchies of numpy arrays to represent the domain decomposition of the application, as well as corresponding groups of processes on the network. You can "map" an application to the network using assignment of through views. The tool is here if anyone is curious: https://github.com/tgamblin/rubik. I used numpy to implement this because I wanted to be able to do mappings for arbitrary-dimensional networks. Blue Gene/Q, for example, has a 5-D network. The reason I bring this up is because I rely on the ndarray.base pointer and some of the semantics in __array_interface__ to translate indices within my hierarchy of views. e.g., if a value is at (0,0) in a view I want to know that it's actually at (4,4) in its immediate parent array. After looking over the commit I linked to above, I realized I'm actually relying on a lot of stuff that's not guaranteed by numpy. I rely on .base pointing to its closest parent, and I rely on __array_interface__.data containing the address of the array's memory and its strides. None of these is guaranteed by the API docs: http://docs.scipy.org/doc/numpy/reference/arrays.interface.html So I guess I have a few questions: 1. Is translating indices between base arrays and views something that would be useful to other people? 2. Is there some better way to do this than using ndarray.base and __array_interface__? 3. What's the numpy philosophy on this? Should views know about their parents or not? They obviously have to know a little bit about their memory, but whether or not they know how they were derived from their owning array is a different question. There was some discussion on the vagueness of .base here: http://thread.gmane.org/gmane.comp.python.numeric.general/51688/focus=51703 But it doesn't look like you're deprecating .base in 1.7, only changing its behavior, which I tend to agree is worse than deprecating it. After thinking about all this, I'm not sure what I would like to happen. I can see the value of not keeping extra references around within numpy, and my domain is pretty different from the ways that I imagine people use numpy. I wouldn't have to change my code much to make it work without .base, but I do rely on __array_interface__. If that doesn't include the address and strides, t think I'm screwed as far as translating indices go. Any suggestions? Thanks! -Todd ______________________________________________________________________ Todd Gamblin, tgamblin(a)llnl.gov, http://people.llnl.gov/gamblin2 CASC @ Lawrence Livermore National Laboratory, Livermore, CA, USA

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turn off square brackets in set_print_options?
by Mark Bakker Dec. 8, 2012

Dec. 8, 2012

Hello List, Is it possible to turn off printing the square brackets in set_print_options? Am I overlooking something? Thanks, Mark

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Fwd: [matplotlib-devel] GitHub attachments
by Nathaniel Smith Dec. 7, 2012

Dec. 7, 2012

Heh, looks like we did the trac migration about a month too soon... ---------- Forwarded message ---------- From: Damon McDougall <damon.mcdougall(a)gmail.com> Date: Fri, Dec 7, 2012 at 8:15 PM Subject: [matplotlib-devel] GitHub attachments To: matplotlib-devel(a)lists.sourceforge.net Did everyone see that GitHub now allows attachments in issue comments? IMO, this is awesome. Attaching a visual *thing* of output when users get unexpected results from plotting calls is a massive plus. Drag and drop right into the browser, too! No more having to upload a file to imgur or dropbox and link it to the issue comment and then have it disappear when you delete it and forgot it was linked to GitHub. Win. -- Damon McDougall http://www.damon-is-a-geek.com Institute for Computational Engineering Sciences 201 E. 24th St. Stop C0200 The University of Texas at Austin Austin, TX 78712-1229 ------------------------------------------------------------------------------ LogMeIn Rescue: Anywhere, Anytime Remote support for IT. Free Trial Remotely access PCs and mobile devices and provide instant support Improve your efficiency, and focus on delivering more value-add services Discover what IT Professionals Know. Rescue delivers http://p.sf.net/sfu/logmein_12329d2d _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel

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