Mailman 3 June 2015 - 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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Generalize hstack/vstack --> stack; Block matrices like in matlab
by Stefan Otte Jan. 9, 2016

Jan. 9, 2016

Hey, quite often I work with block matrices. Matlab offers the convenient notation [ a b; c d ] to stack matrices. The numpy equivalent is kinda clumsy: vstack([hstack([a,b]), hstack([c,d])]) I wrote the little function `stack` that does exactly that: stack([[a, b], [c, d]]) In my case `stack` replaced `hstack` and `vstack` almost completely. If you're interested in including it in numpy I created a pull request [1]. I'm looking forward to getting some feedback! Best, Stefan [1] https://github.com/numpy/numpy/pull/5057

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Video meeting this week
by Nathaniel Smith July 12, 2015

July 12, 2015

Hi all, In a week and a half, this is happening: https://github.com/numpy/numpy/wiki/SciPy-2015-developer-meeting It's somewhat short notice (my bad :-/), but I think it would be good to have a short video meeting sometime this week as a kind of "pre-meeting" -- to at least briefly go over the main issues we see facing the project to prime the pump, get a better idea about what we want to accomplish at the meeting itself, and gather some early feedback from anyone who won't be able to make it to SciPy (we'll miss you). The obligatory doodle: http://doodle.com/6b4s6thqt9xt4vnh Depending on the interest level, I'm thinking we'll either use Google Hangouts or Bluejeans (https://bluejeans.com/ -- same as what Ralf used for the similar SciPy meeting a few months ago; needs a plugin installed but is available for Windows / OS X / 64-bit Linux / Android / iOS, or regular telephone, or h323 softphone). -n -- Nathaniel J. Smith -- http://vorpus.org

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F2PY and multi-dimension arrays - unexpected array size error
by Backing Olof July 1, 2015

July 1, 2015

Hi I am helping out with a Python and Fortran project. Let me give you some background: * Fortran source: C Bergstrom FCC C User subroutine VUMAT subroutine VUMAT( C Read only - * nblock, ndir, nshr, nstatev, nprops, * stepTime, dt, * props, * density, strainInc, * tempOld, * stressOld, stateOld, enerInternOld, enerInelasOld, C Write only - * stressNew, stateNew, enerInternNew, enerInelasNew ) C include 'vaba_param.inc' C dimension props(nprops), 1 density(nblock), strainInc(nblock,ndir+nshr), 2 tempOld(nblock), 5 stressOld(nblock,ndir+nshr), stateOld(nblock,nstatev), 6 enerInternOld(nblock), enerInelasOld(nblock), 2 stressNew(nblock,ndir+nshr), stateNew(nblock,nstatev), 3 enerInternNew(nblock), enerInelasNew(nblock) * Corresponding .pyf integer :: nblock integer :: ndir integer :: nshr integer :: nstatev integer :: nprops real :: steptime real :: dt real dimension(nprops) :: props real dimension(nblock) :: density real dimension(nblock,ndir+nshr) :: straininc real dimension(nblock) :: tempold real dimension(nblock,ndir+nshr) :: stressold real dimension(nblock,nstatev) :: stateold real dimension(nblock) :: enerinternold real dimension(nblock) :: enerinelasold real dimension(nblock,ndir+nshr),intent(out) :: stressnew real dimension(nblock,nstatev),intent(out) :: statenew real dimension(nblock),intent(out) :: enerinternnew real dimension(nblock),intent(out) :: enerinelasnew * Python source with call of Fortran routine: nblock = 1 ndir = 3 nshr = 3 nstatev = 3 nprops = 11 stepTime = 1 dt = 1 props = np.array([10, 0.5, 1e10, 5, 1e12, 3e-6, 8e-6, 27, 2], float) density = np.array([[7.8e3]], float) strainInc = np.array([[1,-0.5,-0.5,0,0,0]], float) tempOld = np.array([[1]], float) stressOld = np.array([[1,1,1,1,1,1]], float) stateOld = np.array([[1,1,1]], float) enerInternOld = np.array([1], float) enerInelasOld = np.array([1], float) stressNew = np.array([[]], float) stateNew = np.array([[]], float) enerInternNew = np.array([[]], float) enerInelasNew = np.array([[]], float) stressNew, stateNew, enerInternNew, enerInelasNew = vumat(nblock, ndir, nshr, nstatev, nprops, stepTime, dt, props, density, strainInc, tempOld, stressOld, stateOld, enerInternOld, enerInelasOld) When trying to run with Python 2.7 I get: olof@ubuntu:~$ ./demo.py unexpected array size: new_size=4, got array with arr_size=1 Traceback (most recent call last): File "./demo.py", line 33, in <module> main() File "./demo.py", line 30, in main stressNew, stateNew, enerInternNew, enerInelasNew = vumat(nblock, ndir, nshr, nstatev, nprops, stepTime, dt, props, density, strainInc, tempOld, stressOld, stateOld, enerInternOld, enerInelasOld) VUMAT_Bergstrom_FCC.error: failed in converting 9th argument `stressold' of VUMAT_Bergstrom_FCC.vumat to C/Fortran array Other stuff: * python 2.7.6 * numpy/f2py 1.8.2 * gcc/gfortran 4.8.2 * ubuntu 14.04 LTS 32-bit I have tried to google, read the f2py manual, fortran tutorials etc, but to no avail. I must also admit that my knowledge in python is so-so and fortran even less(!). What is the missing statement/syntax that I can’t get correct? Your humble programmer, Olof

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Python 3 and isinstance(np.int64(42), int)
by Jens Jørgen Mortensen June 23, 2015

June 23, 2015

Hi! I just finished porting a large code-base to Python 3 (making it work on 2.6, 2.7 and 3.4). It wasn't that difficult, but one thing gave me a hard time and it was this: Python 2.7.9 (default, Apr 2 2015, 15:33:21) [GCC 4.9.2] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> a = np.zeros(7, int) >>> n = a[3] >>> type(n) <type 'numpy.int64'> >>> isinstance(n, int) True With Python 3.4 you get False. I think I understand why (np.int64 is no longer a subclass of int). So, I did this instead: import numbers isinstance(n, numbers.Integral) which works fine (with numpy-1.9). Is this the "correct" way or is there a better way to do it? I would imagine that a lot of code will break because of this - so it would be nice if isinstance(n, int) could be made to work the same way in 2 and 3, but I don't know if this is possible (or desirable). Jens Jørgen

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PR added: frozen dimensions in gufunc signatures
by Jaime Fernández del Río June 23, 2015

June 23, 2015

Hi, I have just sent a PR (https://github.com/numpy/numpy/pull/5015), adding the possibility of having frozen dimensions in gufunc signatures. As a proof of concept, I have added a `cross1d` gufunc to `numpy.core.umath_tests`: In [1]: import numpy as np In [2]: from numpy.core.umath_tests import cross1d In [3]: cross1d.signature Out[3]: '(3),(3)->(3)' In [4]: a = np.random.rand(1000, 3) In [5]: b = np.random.rand(1000, 3) In [6]: np.allclose(np.cross(a, b), cross1d(a, b)) Out[6]: True In [7]: %timeit np.cross(a, b) 10000 loops, best of 3: 76.1 us per loop In [8]: %timeit cross1d(a, b) 100000 loops, best of 3: 13.1 us per loop In [9]: c = np.random.rand(1000, 2) In [10]: d = np.random.rand(1000, 2) In [11]: cross1d(c, d) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-11-72c66212e40c> in <module>() ----> 1 cross1d(c, d) ValueError: cross1d: Operand 0 has a mismatch in its core dimension 0, with gufunc signature (3),(3)->(3) (size 2 is different from 3) The speed up over `np.cross` is nice, and while `np.cross` is not the best of examples, as it needs to handle more sizes, in many cases this will allow producing gufuncs that work without a Python wrapper redoing checks that are best left to the iterator, such as dimension sizes. It still needs tests, but before embarking on fully developing those, I wanted to make sure that there is an interest on this. I would also like to further enhance gufuncs providing computed dimensions, e.g. making it possible to e.g. define `pairwise_cross` with signature '(n, 3)->($m, 3)', where the $ indicates that m is a computed dimension, that would have to be calculated by a function passed to the gufunc constructor and stored in the gufunc object, based on the other core dimensions. In this case it would make $m be n*(n-1), so that all pairwise cross products between 3D vectors could be computed. The syntax with '$' is kind of crappy, so any suggestions on how to better express this in the signature are more than welcome, as well as any feedback on the merits (or lack of them) of implementing this. Jaime -- (\__/) ( O.o) ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.

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Removal of Deprecated Keywords/functionality
by Charles R Harris June 22, 2015

June 22, 2015

Hi All, There are three long ago deprecations that I am not sure how to handle. - keywords skiprows and missing in genfromtxt, deprecated in 1.5. - keyword old_behavior (default False) in correlate. added in 1.5 at least, but default value changed later. The documentation says they will be removed in numpy 2.0, but we might want to try ealier. The case of the correlation function is trickier, as we probabaly need to provide a function with the old behavior before removing the keyword. I've left these cases as is, but the more old stuff hanging about the greater our technical debt. Chuck

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