Mailman 3 January 2014 - NumPy-Discussion

Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem 18 Feb '23

18 Feb '23

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 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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taking a 2D uneven surface slice
by Bryan Woods 04 Dec '14

04 Dec '14

I'm trying to do something that at first glance I think should be simple but I can't quite figure out how to do it. The problem is as follows: I have a 3D grid Values[Nx, Ny, Nz] I want to slice Values at a 2D surface in the Z dimension specified by Z_index[Nx, Ny] and return a 2D slice[Nx, Ny]. It is not as simple as Values[:,:,Z_index]. I tried this: >>> values.shape (4, 5, 6) >>> coords.shape (4, 5) >>> slice = values[:,:,coords] >>> slice.shape (4, 5, 4, 5) >>> slice = np.take(values, coords, axis=2) >>> slice.shape (4, 5, 4, 5) >>> Obviously I could create an empty 2D slice and then fill it by using np.ndenumerate to fill it point by point by selecting values[i, j, Z_index[i, j]]. This just seems too inefficient and not very pythonic.

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building 32 bit numpy on 64 bit linux
by David Jones 19 May '14

19 May '14

I'm trying to compile 32-bit numpy on a 64 bit Centos 6 system, but fails with the message: "Broken toolchain: cannot link a simple C program" It get's the compile flags right, but not the linker: C compiler: gcc -pthread -fno-strict-aliasing -g -O2 -m32 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC compile options: '-Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -Inumpy/core/include -I/opt/python/ia32/include/python2.7 -c' gcc: _configtest.c gcc -pthread _configtest.o -o _configtest _configtest.o: could not read symbols: File in wrong format collect2: ld returned 1 exit status I'm bulding it using a 32bit python build that I compiled on the same system. I tried: OPT="-m32" FOPT="-m32" python setup.py build and setarch x86_64 -B python setup.py build But with the same results. Someone worked around this by altering ccompiler.py, but I'm trying to find a cleaner solution. See:http://stackoverflow.com/questions/11265057/how-do-i-install-a-32-bit-v… Is there a standard way of doing this? Regards, David J.

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Suggestions for GSoC Projects
by jennifer stone 02 Mar '14

02 Mar '14

With GSoC 2014 being round the corner, I hereby put up few projects for discussion that I would love to pursue as a student. Guidance, suggestions are cordially welcome:- 1. If I am not mistaken, contour integration is not supported by SciPy; in fact even line integrals of real functions is yet to be implemented in SciPy, which is surprising. Though we at present have SymPy for line Integrals, I doubt if there is any open-source python package supporting the calculation of Contour Integrals. With integrate module of SciPy already having been properly developed for definite integration, implementation of line as well as contour integrals, I presume; would not require work from scratch and shall be a challenging but fruitful project. 2. I really have no idea if the purpose of NumPy or SciPy would encompass this but we are yet to have indefinite integration. An implementation of that, though highly challenging, may open doors for innumerable other functions like the ones to calculate the Laplace transform, Hankel transform and many more. 3. As stated earlier, we have spherical harmonic functions (with much scope for dev) we are yet to have elliptical and cylindrical harmonic function, which may be developed. 4. Lastly, we are yet to have Inverse Laplace transforms which as Ralf has rightly pointed out it may be too challenging to implement. 5. Further reading the road-map given by Mr.Ralf, I would like to develop the Bluestein's FFT algorithm. Thanks for reading along till the end. I shall append to this mail as when I am struck with ideas. Please do give your valuable guidance

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Silencing NumPy output
by Frédéric Bastien 27 Feb '14

27 Feb '14

Hi, NumPy 1.8 removed the private NumPy interface numpy.distutils.__config__. So a Theano user make a PR to make Theano use the official interface: numpy.distutils.system_info.get_info("blas_opt") But this output many stuff to the output. I can silence part of it by silencing warnings, but I'm not able to silence this output: Found executable /usr/bin/gfortran ATLAS version 3.8.3 built by mockbuild on Wed Jul 28 02:12:34 UTC 2010: UNAME : Linux x86-15.phx2.fedoraproject.org 2.6.32-44.el6.x86_64 #1 SMP Wed Jul 7 15:47:50 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux INSTFLG : -1 0 -a 1 ARCHDEFS : -DATL_OS_Linux -DATL_ARCH_Corei7 -DATL_CPUMHZ=1596 -DATL_SSE3 -DATL_SSE2 -DATL_SSE1 -DATL_USE64BITS -DATL_GAS_x8664 F2CDEFS : -DAdd_ -DF77_INTEGER=int -DStringSunStyle CACHEEDGE: 524288 F77 : gfortran, version GNU Fortran (GCC) 4.5.0 20100716 (Red Hat 4.5.0-3) F77FLAGS : -O -g -Wa,--noexecstack -fPIC -m64 SMC : gcc, version gcc (GCC) 4.5.0 20100716 (Red Hat 4.5.0-3) SMCFLAGS : -fomit-frame-pointer -mfpmath=sse -msse3 -O2 -fno-schedule-insns2 -g -Wa,--noexecstack -fPIC -m64 SKC : gcc, version gcc (GCC) 4.5.0 20100716 (Red Hat 4.5.0-3) SKCFLAGS : -fomit-frame-pointer -mfpmath=sse -msse3 -O2 -fno-schedule-insns2 -g -Wa,--noexecstack -fPIC -m64 -L/opt/lisa/os_v2/canopy/appdata/canopy-1.1.0.1371.rh5-x86_64/../../appdata/canopy-1.1.0.1371.rh5-x86_64/lib -lptf77blas -lptcblas -latlas I tried to redirect the stdout and stderr, but it don't work. I looked into NumPy code and I don't see a way to change that from a library that use NumPy. Is there a way to access to silence that output? Is there a new place of the old interface: numpy.distutils.__config__ that I can reuse? It don't need to be a public interface. thanks Frédéric

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MKL and OpenBLAS
by Dinesh Vadhia 07 Feb '14

07 Feb '14

This conversation gets discussed often with Numpy developers but since the requirement for optimized Blas is pretty common these days, how about distributing Numpy with OpenBlas by default? People who don't want optimized BLAS or OpenBLAS can then edit the site.cfg file to add/remove. I can never remember if Numpy comes with Atlas by default but either way, if using MKL is not feasible because of its licensing issues then Numpy has to be re-compiled with OpenBLAS (for example). Why not make it easier for developers to use Numpy with an in-built optimized Blas. Btw, just in case some folks from Intel are listening: how about releasing MKL binaries for all platforms for developers to do with it what they want ie. free. You know it makes sense!

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De Bruijn sequence
by Vincent Davis 01 Feb '14

01 Feb '14

I happen to be working with De Bruijn sequences. Is there any interest in this being part of numpy/scipy? https://gist.github.com/vincentdavis/8588879 Vincent Davis

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Deprecation of boolean substract and negative (the - operator)
by sebastian 30 Jan '14

30 Jan '14

Hey all, recently we had a small discussion about deprecating some of the operators for boolean arrays. This discussion seemed to have ended by large in the consense that while most boolean operators are well defined and should be kept, the `-` one is not very well defined on boolean arrays and has the problem of the inconsistency: - np.array(False) == True False - np.array(False) == False # leading to: False - (-np.arry(False)) != False + np.array(False) So that it is preferable to use one of the binary operators for this operation. For now this would only be a deprecation, but both operators are probably used out there. So if you have any serious doubt about starting this deprecation please note it here. The Pull request to implement such a deprecation is: https://github.com/numpy/numpy/pull/4105 Regards, Sebastian

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NumPy-Discussion January 2014