Mailman 3 January 2013 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker July 11, 2023

July 11, 2023

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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Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem Feb. 18, 2023

Feb. 18, 2023

Dear all, I have a code using lots of "numpy.where" to make some constrained calculations as in: data = arange(10) result = np.where(data == 0, 0., 1./data) # or data1 = arange(10) data2 = arange(10)+1.0 result = np.where(data1 > data2, np.sqrt(data1-data2), np.sqrt(data2-data2)) which then produces warnings like: /usr/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt or for the first example: /usr/bin/ipython:1: RuntimeWarning: divide by zero encountered in divide How do I avoid these messages to appear? I know that I could in principle use numpy.seterr. However, I do NOT want to remove these warnings for other potential divide/multiply/sqrt etc errors. Only when I am using a "where", to in fact avoid such warnings! Note that the warnings only happen once, but since I am going to release that code, I would like to avoid the user to get such messages which are irrelevant here (because I am testing, with the where, when NOT to divide by zero or take a sqrt of a negative number). thanks! Eric

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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell July 22, 2021

July 22, 2021

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 March 31, 2016

March 31, 2016

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 June 4, 2013

June 4, 2013

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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pyhdf packaging
by Andreas Hilboll May 23, 2013

May 23, 2013

Hi, I would like to package pyhdf for Ubuntu and make the package publicly available. Since the license is not totally clear to me (I cannot find any information in the sources, and the cheeseshop says "public", which doesn't mean anything to me), I tried to contact the maintainer, Andre Gosselin, however the email bounces, so I guess he's gone. Can anyone point me to how to proceed from here? Cheers, Andreas.

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swig interface file (numpy.i) warning
by Tom Krauss Feb. 27, 2013

Feb. 27, 2013

Hi, I've been happy to use numpy.i for generating SWIG interfaces to C++. For a while, I've noticed this warning while compiling: /Users/tkrauss/projects/dev_env/lib/python2.7/site-packages/numpy-1.8.0.dev_f2f0ac0_20120725-py2.7-macosx-10.8-x86_64.egg/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: #warning "Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" and today tried to get rid of the warning. So, in numpy.i, I followed the warning's advice. I added the # def here: %{ #ifndef SWIG_FILE_WITH_INIT # define NO_IMPORT_ARRAY #endif #include "stdio.h" *#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION* #include <numpy/arrayobject.h> %} SWIG was happy, but when compiling the C++ wrapper, there were many warnings followed by many errors. The warnings were for redefinition of NPY_MIN_BYTE and similar. The errors were for all kinds of stuff, excerpt here: native_wrap.cpp:3632: error: ‘PyArray_NOTYPE’ was not declared in this scope native_wrap.cpp:3633: error: cannot convert ‘PyObject*’ to ‘const PyArrayObject*’ for argument ‘1’ to ‘int PyArray_TYPE(const PyArrayObject*)’ native_wrap.cpp: At global scope: native_wrap.cpp:3877: error: ‘intp’ has not been declared native_wrap.cpp: In function ‘int require_fortran(PyArrayObject*)’: native_wrap.cpp:3929: error: ‘struct tagPyArrayObject’ has no member named ‘nd’ native_wrap.cpp:3933: error: ‘struct tagPyArrayObject’ has no member named ‘flags’ native_wrap.cpp:3933: error: ‘FARRAY’ was not declared in this scope native_wrap.cpp:20411: error: ‘struct tagPyArrayObject’ has no member named ‘data’ It looks like there is a new C API for numpy, and the version of numpy.i that I have doesn't use it. Is there a new version of numpy.i available (or in development) that works with the new API? Short term it will just get rid of a warning but I am interested in a good long term solution in case I need to upgrade numpy. Thanks, Tom

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pull request: generalized ufunc signature fix and lineal algebra generalized ufuncs
by Oscar Villellas Feb. 20, 2013

Feb. 20, 2013

Hello, At Continuum Analytics we've been working on a submodule implementing a set of lineal algebra operations as generalized ufuncs. This allows specifying arrays of lineal algebra problems to be computed with a single Python call, allowing broadcasting as well. As the vectorization is handled in the kernel, this gives a speed edge on the operations. We think this could be useful to the community and we want to share the work done. I've created a couple of pull-requests: The first one contains a fix for a bug in the handling of certain signatures in the gufuncs. This was found while building the submodule. The fix was done by Mark Wiebe, so credit should go to him :). https://github.com/numpy/numpy/pull/2953 The second pull request contains the submodule itself and builds on top of the previous fix. It contains a rst file that explains the submodule, enumerates the functions implemented and details some implementation bits. The entry point to the module is in written in Python and contains detailed docstrings. https://github.com/numpy/numpy/pull/2954 We are open to discussion and to make improvements to the code if needed, in order to adapt to NumPy standards. Thanks, Oscar.

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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 Feb. 13, 2013

Feb. 13, 2013

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 Feb. 13, 2013

Feb. 13, 2013

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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