Mailman 3 October 2016 - 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 … [View More]

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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 … [View More]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 [View Less]

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automatically avoiding temporary arrays
by Julian Taylor Feb. 27, 2017

Feb. 27, 2017

hi, Temporary arrays generated in expressions are expensive as the imply extra memory bandwidth which is the bottleneck in most numpy operations. For example: r = a + b + c creates the b + c temporary and then adds a to it. This can be rewritten to be more efficient using inplace operations: r = b + c r += a This saves some memory bandwidth and can speedup the operation by 50% for very large arrays or even more if the inplace operation allows it to be completed completely in the cpu cache. … [View More]

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Re: [Numpy-discussion] Integers to negative integer powers, time for a decision.
by Peter Creasey Jan. 3, 2017

Jan. 3, 2017

> On Sun, Oct 9, 2016 at 12:59 PM, Stephan Hoyer <shoyer(a)gmail.com> wrote: > >> >> I agree with Sebastian and Nathaniel. I don't think we can deviating from >> the existing behavior (int ** int -> int) without breaking lots of existing >> code, and if we did, yes, we would need a new integer power function. >> >> I think it's better to preserve the existing behavior when it gives >> sensible results, and error when it doesn't. Adding … [View More]

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__numpy_ufunc__
by Charles R Harris Oct. 31, 2016

Oct. 31, 2016

Hi All, Does anyone remember discussion of numpy scalars apropos __numpy_ufunc__? Chuck

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Problem with compiling openacc with f2py
by Vikram Singh Oct. 30, 2016

Oct. 30, 2016

I am a newbie to f2py so I have been creating simple test cases. Eventually I want to be able to use openacc subroutine from python. So here's the test case module test use iso_c_binding, only: sp => C_FLOAT, dp => C_DOUBLE, i8 => C_INT use omp_lib use openacc implicit none contains subroutine add_acc (a, b, n, c) integer(kind=i8), intent(in) :: n real(kind=dp), intent(in) :: a(n) real(kind=dp), intent(in) :: b(n)… [View More] real(kind=dp), intent(out) :: c(n) integer(kind=i8) :: i !$acc kernels do i = 1, n c(i) = a(i) + b(i) end do !$acc end kernels end subroutine add_acc subroutine add_omp (a, b, n, c) integer(kind=i8), intent(in) :: n real(kind=dp), intent(in) :: a(n) real(kind=dp), intent(in) :: b(n) real(kind=dp), intent(out) :: c(n) integer(kind=i8) :: i, j !$omp parallel do do i = 1, n c(i) = a(i) + b(i) end do !$omp end parallel do end subroutine add_omp subroutine nt (c) integer(kind=i8), intent(out) :: c c = omp_get_max_threads() end subroutine nt subroutine mult (a, b, c) real(kind=dp), intent(in) :: a real(kind=dp), intent(in) :: b real(kind=dp), intent(out) :: c c = a * b end subroutine mult end module test I compile using f2py -c -m --f90flags='-fopenacc -foffload=nvptx-none -foffload=-O3 -O3 -fPIC' hello hello.f90 -L/usr/local/cuda/lib64 -lcublas -lcudart -lgomp Now, until I add the acc directives everything works fine. But as soon as I add the acc directives I get this error. gfortran:f90: /tmp/tmpld6ssow3/src.linux-x86_64-3.5/hello-f2pywrappers2.f90 /home//Experiments/Nvidia/OpenACC/OLCFHack15/gcc6/install/bin/gfortran -Wall -g -Wall -g -shared /tmp/tmpld6ssow3/tmp/tmpld6ssow3/src.linux-x86_64-3.5/hellomodule.o /tmp/tmpld6ssow3/tmp/tmpld6ssow3/src.linux-x86_64-3.5/fortranobject.o /tmp/tmpld6ssow3/hello.o /tmp/tmpld6ssow3/tmp/tmpld6ssow3/src.linux-x86_64-3.5/hello-f2pywrappers2.o -L/usr/local/cuda/lib64 -L/home//usr/local/miniconda/lib -lcublas -lcudart -lgomp -lpython3.5m -lgfortran -o ./hello.cpython-35m-x86_64-linux-gnu.so /usr/bin/ld: /tmp/cc2yQ89d.target.o: relocation R_X86_64_32 against `.rodata' can not be used when making a shared object; recompile with -fPIC /tmp/cc2yQ89d.target.o: error adding symbols: Bad value collect2: error: ld returned 1 exit status /usr/bin/ld: /tmp/cc2yQ89d.target.o: relocation R_X86_64_32 against `.rodata' can not be used when making a shared object; recompile with -fPIC /tmp/cc2yQ89d.target.o: error adding symbols: Bad value collect2: error: ld returned 1 exit status error: Command "/home//Experiments/Nvidia/OpenACC/OLCFHack15/gcc6/install/bin/gfortran -Wall -g -Wall -g -shared /tmp/tmpld6ssow3/tmp/tmpld6ssow3/src.linux-x86_64-3.5/hellomodule.o /tmp/tmpld6ssow3/tmp/tmpld6ssow3/src.linux-x86_64-3.5/fortranobject.o /tmp/tmpld6ssow3/hello.o /tmp/tmpld6ssow3/tmp/tmpld6ssow3/src.linux-x86_64-3.5/hello-f2pywrappers2.o -L/usr/local/cuda/lib64 -L/home//usr/local/miniconda/lib -lcublas -lcudart -lgomp -lpython3.5m -lgfortran -o ./hello.cpython-35m-x86_64-linux-gnu.so" failed with exit status 1 I don't get why just putting acc directives should create errors, when omp does not. Vikram [View Less]

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Numpy scalar integers to negative scalar integer powers.
by Charles R Harris Oct. 28, 2016

Oct. 28, 2016

Hi All, I've put up a PR to deal with the numpy scalar integer powers at https://github.com/numpy/numpy/pull/8221. Note that for now everything goes through the np.power function. Chuck

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How to use user input as equation directly
by djxvillain Oct. 28, 2016

Oct. 28, 2016

Hello all, I am an electrical engineer and new to numpy. I need the ability to take in user input, and use that input as a variable. For example: t = input('enter t: ') x = input('enter x: ') I need the user to be able to enter something like x = 2*np.sin(2*np.pi*44100*t+np.pi/2) and it be the same as if they just typed it in the .py file. There's no clean way to cast or evaluate it that I've found. I could make a function to parse this string character by character, but I figured this … [View More]

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Re: [Numpy-discussion] padding options for diff
by Peter Creasey Oct. 28, 2016

Oct. 28, 2016

> Date: Wed, 26 Oct 2016 16:18:05 -0400 > From: Matthew Harrigan <harrigan.matthew(a)gmail.com> > > Would it be preferable to have to_begin='first' as an option under the > existing kwarg to avoid overlapping? > >> if keep_left: >> if to_begin is None: >> to_begin = np.take(a, [0], axis=axis) >> else: >> raise ValueError(?np.diff(a, keep_left=False, to_begin=None) >> can be used with either keep_left or to_begin,… [View More]

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