Mailman 3 September 2012 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker 11 Jul '23

11 Jul '23

I've been browsing the numpy source. I'm wondering about mixed-mode arithmetic on arrays. I believe the way numpy handles this is that it never does mixed arithmetic, but instead converts arrays to a common type. Arguably, that might be efficient for a mix of say, double and float. Maybe not. But for a mix of complex and a scalar type (say, CDouble * Double), it's clearly suboptimal in efficiency. So, do I understand this correctly? If so, is that something we should improve?

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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell 22 Jul '21

22 Jul '21

David Froger <david.froger.info <at> gmail.com> writes: > Hy,My question is about reading Fortran binary file (oh no this question > again...) I've posted this before, but I finally got it cleaned up for the Cookbook. For this purpose I use a subclass of file that has methods for reading unformatted Fortran data. See http://www.scipy.org/Cookbook/FortranIO/FortranFile. I'd gladly see this in numpy or scipy somewhere, but I'm not sure where it belongs. > program makeArray > implicit none > integer,parameter:: nx=10,ny=20 > real(4),dimension(nx,ny):: ux,uy,p > integer :: i,j > open(11,file='uxuyp.bin',form='unformatted') > do i = 1,nx > do j = 1,ny > ux(i,j) = real(i*j) > uy(i,j) = real(i)/real(j) > p (i,j) = real(i) + real(j) > enddo > enddo > write(11) ux,uy > write(11) p > close(11) > end program makeArray When I run the above program compiled with gfortran on my Intel Mac, I can read it back with:: >>> import numpy as np >>> from fortranfile import FortranFile >>> f=FortranFile('uxuyp.bin', endian='<') >>> uxuy = f.readReals(prec='f') # 'f' for default reals >>> len(uxuy) 400 >>> ux = np.array(uxuy[:200]).reshape((20,10)).T >>> uy = np.array(uxuy[200:]).reshape((20,10)).T >>> p = f.readReals('f').reshape((20,10)).T >>> ux array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.], [ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24., 26., 28., 30., 32., 34., 36., 38., 40.], [ 3., 6., 9., 12., 15., 18., 21., 24., 27., 30., 33., 36., 39., 42., 45., 48., 51., 54., 57., 60.], [ 4., 8., 12., 16., 20., 24., 28., 32., 36., 40., 44., 48., 52., 56., 60., 64., 68., 72., 76., 80.], [ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.], [ 6., 12., 18., 24., 30., 36., 42., 48., 54., 60., 66., 72., 78., 84., 90., 96., 102., 108., 114., 120.], [ 7.,Proxy-Connection: keep-alive Cache-Control: max-age=0 14., 21., 28., 35., 42., 49., 56., 63., 70., 77., 84., 91., 98., 105., 112., 119., 126., 133., 140.], [ 8., 16., 24., 32., 40., 48., 56., 64., 72., 80., 88., 96., 104., 112., 120., 128., 136., 144., 152., 160.], [ 9., 18., 27., 36., 45., 54., 63., 72., 81., 90., 99., 108., 117., 126., 135., 144., 153., 162., 171., 180.], [ 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., 110., 120., 130., 140., 150., 160., 170., 180., 190., 200.]]) >>> uy array([[ 1. , 0.5 , 0.33333334, 0.25 , 0.2 , 0.16666667, 0.14285715, 0.125 , 0.11111111, 0.1 , 0.09090909, 0.08333334, 0.07692308, 0.07142857, 0.06666667, 0.0625 , 0.05882353, 0.05555556, 0.05263158, 0.05 ], [ 2. , 1. , 0.66666669, 0.5 , 0.40000001, 0.33333334, 0.2857143 , 0.25 , 0.22222222, 0.2 , 0.18181819, 0.16666667, 0.15384616, 0.14285715, 0.13333334, 0.125 , 0.11764706, 0.11111111, 0.10526316, 0.1 ], [ 3. , 1.5 , 1. , 0.75 , 0.60000002, 0.5 , 0.42857143, 0.375 , 0.33333334, 0.30000001, 0.27272728, 0.25 , 0.23076923, 0.21428572, 0.2 , 0.1875 , 0.17647059, 0.16666667, 0.15789473, 0.15000001], [ 4. , 2. , 1.33333337, 1. , 0.80000001, 0.66666669, 0.5714286 , 0.5 , 0.44444445, 0.40000001, 0.36363637, 0.33333334, 0.30769232, 0.2857143 , 0.26666668, 0.25 , 0.23529412, 0.22222222, 0.21052632, 0.2 ], [ 5. , 2.5 , 1.66666663, 1.25 , 1. , 0.83333331, 0.71428573, 0.625 , 0.55555558, 0.5 , 0.45454547, 0.41666666, 0.38461539, 0.35714287, 0.33333334, 0.3125 , 0.29411766, 0.27777779, 0.2631579 , 0.25 ], [ 6. , 3. , 2. , 1.5 , 1.20000005, 1. , 0.85714287, 0.75 , 0.66666669, 0.60000002, 0.54545456, 0.5 , 0.46153846, 0.42857143, 0.40000001, 0.375 , 0.35294119, 0.33333334, 0.31578946, 0.30000001], [ 7. , 3.5 , 2.33333325, 1.75 , 1.39999998, 1.16666663, 1. , 0.875 , 0.77777779, 0.69999999, 0.63636363, 0.58333331, 0.53846157, 0.5 , 0.46666667, 0.4375 , 0.41176471, 0.3888889 , 0.36842105, 0.34999999], [ 8. , 4. , 2.66666675, 2. , 1.60000002, 1.33333337, 1.14285719, 1. , 0.8888889 , 0.80000001, 0.72727275, 0.66666669, 0.61538464, 0.5714286 , 0.53333336, 0.5 , 0.47058824, 0.44444445, 0.42105263, 0.40000001], [ 9. , 4.5 , 3. , 2.25 , 1.79999995, 1.5 , 1.28571427, 1.125 , 1. , 0.89999998, 0.81818181, 0.75 , 0.69230771, 0.64285713, 0.60000002, 0.5625 , 0.52941179, 0.5 , 0.47368422, 0.44999999], [ 10. , 5. , 3.33333325, 2.5 , 2. , 1.66666663, 1.42857146, 1.25 , 1.11111116, 1. , 0.90909094, 0.83333331, 0.76923078, 0.71428573, 0.66666669, 0.625 , 0.58823532, 0.55555558, 0.52631581, 0.5 ]]) >>> p array([[ 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.], [ 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.], [ 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.], [ 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.], [ 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.], [ 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.], [ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.], [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28.], [ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.], [ 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]]) Note that you have to provide the shape information for ux and uy because fortran writes them together as a stream of 400 numbers. -Neil

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C-API: multidimensional array indexing?
by Johann Bauer 31 Mar '16

31 Mar '16

Dear experts, is there a C-API function for numpy which implements Python's multidimensional indexing? Say, I have a 2d-array PyArrayObject * M; and an index int i; how do I extract the i-th row or column M[i,:] respectively M[:,i]? I am looking for a function which gives again a PyArrayObject * and which is a view to M (no copied data; the result should be another PyArrayObject whose data and strides points to the correct memory portion of M). I searched the API documentation, Google and mailing lists for quite a long time but didn't find anything. Can you help me? Thanks, Johann

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SVD errors
by mtrumpis＠berkeley.edu 04 Jun '13

04 Jun '13

Hello list.. I've run into two SVD errors over the last few days. Both errors are identical in numpy/scipy. I've submitted a ticket for the 1st problem (numpy ticket #990). Summary is: some builds of the lapack_lite module linking against system LAPACK (not the bundled dlapack_lite.o, etc) give a "LinAlgError: SVD did not converge" exception on my matrix. This error does occur using Mac's Accelerate framework LAPACK, and a coworker's Ubuntu LAPACK version. It does not seem to happen using ATLAS LAPACK (nor using Octave/Matlab on said Ubuntu) Just today I've come across a negative singular value cropping up in an SVD of a different matrix. This error does occur on my ATLAS LAPACK based numpy, as well as on the Ubuntu setup. And once again, it does not happen in Octave/Matlab. I'm using numpy 1.3.0.dev6336 -- don't know what the Ubuntu box is running. Here are some npy files for the two different cases: https://cirl.berkeley.edu/twiki/pub/User/MikeTrumpis/noconverge_operator.npy https://cirl.berkeley.edu/twiki/pub/User/MikeTrumpis/negsval_operator.npy Mike

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Fwd: Package: scipy-0.11.0-0.1.rc2.fc18 Tag: f18-updates-candidate Status: failed Built by: orion
by Orion Poplawski 13 Feb '13

13 Feb '13

This is a plea for some help. We've been having trouble getting scipy to pass all of the tests in the Fedora 18 build with python 3.3 (although it seems to build okay in Fedora 19). Below are the logs of the build. There appears to be some kind of memory corruption that manifests itself a little differently on 32-bit vs. 64-bit. I really have no idea myself how to pursue debugging this, though I'm happy to provide any more needed information. Thanks, Orion -------- Original Message -------- Subject: Package: scipy-0.11.0-0.1.rc2.fc18 Tag: f18-updates-candidate Status: failed Built by: orion Date: Thu, 20 Sep 2012 21:01:54 +0000 (UTC) From: Fedora Koji Build System <buildsys(a)fedoraproject.org> To: ausil(a)fedoraproject.org, jspaleta(a)fedoraproject.org, voronov(a)fedoraproject.org, torwangjl(a)fedoraproject.org, alagunambi(a)fedoraproject.org, urkle(a)fedoraproject.org, orion(a)fedoraproject.org Package: scipy-0.11.0-0.1.rc2.fc18 Tag: f18-updates-candidate Status: failed Built by: orion ID: 350761 Started: Thu, 20 Sep 2012 20:39:45 UTC Finished: Thu, 20 Sep 2012 21:01:32 UTC scipy-0.11.0-0.1.rc2.fc18 (350761) failed on buildvm-33.phx2.fedoraproject.org (x86_64), buildvm-35.phx2.fedoraproject.org (i386), buildvm-34.phx2.fedoraproject.org (noarch): BuildError: error building package (arch x86_64), mock exited with status 1; see build.log for more information SRPMS: scipy-0.11.0-0.1.rc2.fc18.src.rpm Failed tasks: ------------- Task 4509076 on buildvm-33.phx2.fedoraproject.org Task Type: buildArch (scipy-0.11.0-0.1.rc2.fc18.src.rpm, x86_64) logs: http://koji.fedoraproject.org/koji/getfile?taskID=4509076&name=build.log http://koji.fedoraproject.org/koji/getfile?taskID=4509076&name=mock_output.… http://koji.fedoraproject.org/koji/getfile?taskID=4509076&name=root.log http://koji.fedoraproject.org/koji/getfile?taskID=4509076&name=state.log Task 4509077 on buildvm-35.phx2.fedoraproject.org Task Type: buildArch (scipy-0.11.0-0.1.rc2.fc18.src.rpm, i686) logs: http://koji.fedoraproject.org/koji/getfile?taskID=4509077&name=build.log http://koji.fedoraproject.org/koji/getfile?taskID=4509077&name=mock_output.… http://koji.fedoraproject.org/koji/getfile?taskID=4509077&name=root.log http://koji.fedoraproject.org/koji/getfile?taskID=4509077&name=state.log Task 4509063 on buildvm-34.phx2.fedoraproject.org Task Type: build (f18-candidate, /scipy:cb69bd06f0d930fbe8840d89b918b617e28af63f) Closed tasks: ------------- Task 4509064 on buildvm-30.phx2.fedoraproject.org Task Type: buildSRPMFromSCM (/scipy:cb69bd06f0d930fbe8840d89b918b617e28af63f) logs: http://koji.fedoraproject.org/koji/getfile?taskID=4509064&name=build.log http://koji.fedoraproject.org/koji/getfile?taskID=4509064&name=checkout.log http://koji.fedoraproject.org/koji/getfile?taskID=4509064&name=mock_output.… http://koji.fedoraproject.org/koji/getfile?taskID=4509064&name=root.log http://koji.fedoraproject.org/koji/getfile?taskID=4509064&name=state.log Task Info: http://koji.fedoraproject.org/koji/taskinfo?taskID=4509063 Build Info: http://koji.fedoraproject.org/koji/buildinfo?buildID=350761

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example reading binary Fortran file
by David Froger 13 Feb '13

13 Feb '13

Hy, My question is about reading Fortran binary file (oh no this question again...) Until now, I was using the unpack module like that : def lread(f,fourBeginning,fourEnd,*tuple): from struct import unpack """Reading a Fortran binary file in litte-endian""" if fourBeginning: f.seek(4,1) for array in tuple: for elt in xrange(array.size): transpose(array).flat[elt] = unpack(array.dtype.char,f.read(array.itemsize))[0] if fourEnd: f.seek(4,1) After googling, I read that fopen and npfille was deprecated, and we should use numpy.fromfile and ndarray.tofile, but despite of the documentaion, the cookbook, the mailling list and google, I don't succed in making a simple example. Considering the simple Fortran code below what is the Python script to read the four arrrays? What about if my pc is little endian and the file big endian? I think it will be a good idea to put the Fortran writting-arrays code and the Python reading-array script in the cookbook and maybe a page to help people comming from Fortran to start with Python ? Best, David Froger program makeArray implicit none integer,parameter:: nx=10,ny=20 real(4),dimension(nx,ny):: ux,uy,p integer :: i,j open(11,file='uxuyp.bin',form='unformatted') do i = 1,nx do j = 1,ny ux(i,j) = real(i*j) uy(i,j) = real(i)/real(j) p (i,j) = real(i) + real(j) enddo enddo write(11) ux,uy write(11) p close(11) end program makeArray

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Should abs([nan]) be supported?
by Ondřej Čertík 14 Nov '12

14 Nov '12

Hi, When running the test suite, there are problems of this kind: https://github.com/numpy/numpy/issues/394 which then causes for example the Debian buildbots tests to fail (https://github.com/numpy/numpy/issues/406). The problem is really simple: >>> from numpy import array, abs, nan >>> a = array([1, nan, 3]) >>> a array([ 1., nan, 3.]) >>> abs(a) __main__:1: RuntimeWarning: invalid value encountered in absolute array([ 1., nan, 3.]) See the issue #394 for detailed explanation why "nan" is being passed to abs(). Now the question is, what should the right fix be? 1) Should the runtime warning be disabled? 2) Should the tests be reworked, so that "nan" is not tested in allclose()? 3) Should abs() be fixed to not emit the warning? 4) Should the test suite be somehow fixed not to fail if there are runtime warnings? Let me know which direction we should go. Thanks, Ondrej

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Double-ended queues
by William Furnass 09 Nov '12

09 Nov '12

Hi all, I want to be able to within a loop a) apply a mathematical operation to all elements in a vector (can be done atomically) then b) pop zero or more elements from one end of the vector and c) push zero or more elements on to the other end. So far I've used a collections.deque to store my vector as it should be more efficient than a numpy array for the appending and deletion of elements. However, I was wondering whether performance could be improved through the use of a homogeneously-typed double-ended queue i.e. a linked list equivalent of numpy.ndarray. Has anyone previously considered whether it would be worth including such a thing within the numpy package? Cheers, Will

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Issue tracking
by Charles R Harris 23 Oct '12

23 Oct '12

Hi All, The issue tracking discussion seems to have died. Since github issues looks to be a viable alternative at this point, I propose to turn it on for the numpy repository and start directing people in that direction. Thoughts? Chuck

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how is y += x computed when y.strides = (0, 8) and x.strides=(16, 8) ?
by Sebastian Walter 17 Oct '12

17 Oct '12

Hi, I'm using numpy 1.6.1 on Ubuntu 12.04.1 LTS. A code that used to work with an older version of numpy now fails with an error. Were there any changes in the way inplace operations like +=, *=, etc. work on arrays with non-standard strides? For the script: ------- start of code ------- import numpy x = numpy.arange(6).reshape((3,2)) y = numpy.arange(2) print 'x=\n', x print 'y=\n', y u,v = numpy.broadcast_arrays(x, y) print 'u=\n', u print 'v=\n', v print 'v.strides=\n', v.strides v += u print 'v=\n', v # expectation: v = [[6,12], [6,12], [6,12]] print 'u=\n', u print 'y=\n', y # expectation: y = [6,12] ------- end of code ------- I get the output -------- start of output --------- x= [[0 1] [2 3] [4 5]] y= [0 1] u= [[0 1] [2 3] [4 5]] v= [[0 1] [0 1] [0 1]] v.strides= (0, 8) v= [[4 6] [4 6] [4 6]] u= [[0 1] [2 3] [4 5]] y= [4 6] -------- end of output -------- I would have expected that v += u performs an element-by-element += v[0,0] += u[0,0] # increments y[0] v[0,1] += u[0,1] # increments y[1] v[1,0] += u[1,0] # increments y[0] v[1,1] += u[1,1] # increments y[1] v[2,0] += u[2,0] # increments y[0] v[2,1] += u[2,1] # increments y[1] yielding the result y = [6,12] but instead one obtains y = [4, 6] which could be the result of v[2,0] += u[2,0] # increments y[0] v[2,1] += u[2,1] # increments y[1] Is this the intended behavior? regards, Sebastian

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