Mailman 3 January 2019 - 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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Does numpy depend upon a Fortran library?
by Marc F Paterno April 11, 2019

April 11, 2019

Hello, I have encountered a problem with a binary incompatibility between the Fortran runtime library installed with numpy when using 'pip install --user numpy', and that used by the rest of my program, which is built using gfortran from GCC 8.2. The numpy installation uses libgfortran.5.dylib, and GCC 8.2 provides libgfortran.5.dylib. While investigating the source of this problem, I downloaded the numpy source (https://files.pythonhosted.org/packages/04/b6/d7faa70a3e3eac39f943cc6a6a64c…), and tried building it. The resulting libraries have no coupling to any Fortran library that I can find. I can find no Fortran source code files in the numpy source, except in tests or documentation. I am working on a MacBook laptop, running macOS Mojave, and so am using the Accelerate framework to supply BLAS. I do not understand why the pip installation of numpy includes a Fortran runtime library. Can someone explain to me what I am missing? thanks Marc

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[ANN] pvlib-python v0.6.1: predicting power from PV (renweable solar energy)
by Mark Alexander Mikofski Jan. 31, 2019

Jan. 31, 2019

pvlib-0.6.1 is now available PyPI: https://pypi.org/project/pvlib/ Read the Docs: https://pvlib-python.readthedocs.io/en/latest/ GitHub: https://github.com/pvlib/pvlib-python -- Mark Mikofski, PhD (2005) *Fiat Lux*

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Compiling openmp implemented fortran code by f2py using ifort compiler
by koushik naskar Jan. 30, 2019

Jan. 30, 2019

Hi, I have a FORTRAN90 code, with openmp implemented for some parts. Now to compile the fortran code using f2py with openmp support I have to pass the f90 compiler flags. Now the code should compile with openmp support only if I provide the openmp relavant flag, unless it must be compiled as a normal serial code. This works as expected for gfortran but for ifort it doesn't. Lets explain this, if I run, (gfortran serial mode) *f2py -c adt.f90 -m adt_module* Gives output (last few lines) *Fortran f77 compiler: /usr/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops* *Fortran f90 compiler: /usr/bin/gfortran -Wall -g -fno-second-underscore -fPIC -O3 -funroll-loops* *Fortran fix compiler: /usr/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -Wall -g -fno-second-underscore -fPIC -O3 -funroll-loops* (gfortran parallel mode) *f2py -c adt.f90 -m adt_module --f90flags='-fopenmp' -lgomp * output *Fortran f77 compiler: /usr/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops* *Fortran f90 compiler: /usr/bin/gfortran -fopenmp -fPIC -O3 -funroll-loops* *Fortran fix compiler: /usr/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -fopenmp -fPIC -O3 -funroll-loops* As you can clearly see there is no -fopenmp flag during the compilation in serial mode, as expected as I haven't pass the required flag *Now for ifort* (ifort parallel mode) *f2py -c adt.f90 -m adt_module --fcompiler=intelem --f90flags='-qopenmp' -liomp5* Output *Fortran f77 compiler: /opt/intel/compilers_and_libraries_2017.4.196/linux/bin/intel64/ifort -FI -fPIC -fp-model strict -O1 -qopenmp * *Fortran f90 compiler: /opt/intel/compilers_and_libraries_2017.4.196/linux/bin/intel64/ifort -FR -qopenmp -fPIC -fp-model strict -O1 -qopenmp * *Fortran fix compiler: /opt/intel/compilers_and_libraries_2017.4.196/linux/bin/intel64/ifort -FI -qopenmp -fPIC -fp-model strict -O1 -qopenmp* (ifort serial mode) *f2py -c adt.f90 -m adt_module --fcompiler=intelem* Output *Fortran f77 compiler: /opt/intel/compilers_and_libraries_2017.4.196/linux/bin/intel64/ifort -FI -fPIC -fp-model strict -O1 -qopenmp * *Fortran f90 compiler: /opt/intel/compilers_and_libraries_2017.4.196/linux/bin/intel64/ifort -FR -fPIC -fp-model strict -O1 -qopenmp * *Fortran fix compiler: /opt/intel/compilers_and_libraries_2017.4.196/linux/bin/intel64/ifort -FI -fPIC -fp-model strict -O1 -qopenmp* Now, here's the problem, see here compiling is done with -qopenmp flag for serial mode but I haven't passed it in command line. Why is it happening when compiling with ifort but not with gfortran ? And how to resolve this?

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Reminder: weekly status meeting Jan 30 at 12:00 pacific time
by Matti Picus Jan. 30, 2019

Jan. 30, 2019

The draft agenda is at https://hackmd.io/UnnvbPNpSm-gRpbfTK7cmg?both# There is a section for community suggested topics, feel free to join the conversation and add in topics that need attention. The BIDS team

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Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 148, Issue 27
by Yuping Wang Jan. 30, 2019

Jan. 30, 2019

I start to learn python from 《Numpy Beginner's Guide》second edition written by Ivan Idris now I know what the problem is ,the reason is that Numpy converts 2000 to an `int32`.When I use code `a=np.arange(2000, dtype='int64')**3 that you taught me, the calculation is correct thank you all ! Yuping Wang

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Numpy-discussion
by Yuping Wang Jan. 30, 2019

Jan. 30, 2019

Dear Nmupy developers and users: I am a new user of Numpy ,I have encountered a question about Numpy recently, which need your help. The question is below: I don know why there are negative numbers can somebody explain to me why these happens Thanks in advance!

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Re: [Numpy-discussion] negative numbers
by Slavin, Jonathan Jan. 29, 2019

Jan. 29, 2019

I'm not sure which numpy version you're using (I don't get that using numpy 1.14.3), but the issue is that for some reason the data type (dtype) of the array is being assumed to be a 4 byte integer or '<i4'. The maximum value for that is 2,147,483,647 whereas 1999**3 = 7,988,005,999. To get the correct result you can specify the dtype: a = np.arange(2000,dtype='<i8')**3 for example. Regards, Jon On Tue, Jan 29, 2019 at 2:54 AM <numpy-discussion-request(a)python.org> wrote: > From: Yuping Wang <wyupinghhu(a)163.com> > To: numpy-discussion(a)python.org > Cc: > Bcc: > Date: Tue, 29 Jan 2019 15:22:00 +0800 (CST) > Subject: [Numpy-discussion] Numpy-discussion > Dear Nmupy developers and users: > I am a new user of Numpy ,I have encountered a question about > Numpy recently, which need your help. The question is below: > > > > > I don know why there are negative numbers > can somebody explain to me why these happens > Thanks in advance! > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > -- Jonathan D. Slavin Astrophysicist - High Energy Astrophysics Division Center for Astrophysics | Harvard & Smithsonian Office: (617) 496-7981 | Cell: (781) 363-0035 60 Garden Street | MS 83 | Cambridge, MA 02138

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