Mailman 3 July 2019 - 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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Code review for adding axis argument to permutation and shuffle function
by Kexuan Sun 13 Sep '19

13 Sep '19

Hi, I would like to request a code review. The random.permutation and random.shuffle functions now can only shuffle along the first axis of a multi-dimensional array. I propose to add an axis argument for the functions and allow them to shuffle along a given axis. Here is the link to the PR (https://github.com/numpy/numpy/pull/13829). Thanks!

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NEP 28 — A standard community policy for dropping support of old Python and NumPy versions
by Thomas Caswell 06 Aug '19

06 Aug '19

Folks, This NEP is a proposing a standard policy for the community to determine when we age-out support for old versions of Python. This came out of in-person discussions at SciPy earlier in July and scattered discussion across github. This is being proposed by maintainers from Matplotlib, scikit-learn, IPython, Jupyter, yt, SciPy, NumPy, and scikit-image. TL;DR: We propose only supporting versions of CPython initially released in the preceding 42 months of a major or minor release of any of our projects. Please see https://github.com/numpy/numpy/pull/14086 and keep the discussion there as there are many interested parties (from the other projects) that may not be subscribed to the numpy mailing list. Tom -- Thomas Caswell tcaswell(a)gmail.com

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Perturbations in Polynomial roots
by Cédric Hannotier 31 Jul '19

31 Jul '19

Dear all, I am not sure if I misunderstood the Polynomial.roots() method, but I get strange behavior. The roots are way outside of the expected values. That is not the case with np.roots(). What am I doing wrong? Code: import numpy as np def roots1(coef, x, y): for i in range(y.size): coefN = coef.copy() coefN[0] -= y[i] print(np.roots(coefN[::-1]) - x[i]) def roots2(coef, x, y): for i in range(y.size): print((np.polynomial.polynomial.Polynomial(coef) - y[i]).roots() - x[i]) x = np.arange(4) * 0.1 alpha, beta, gamma = 0.16, 1, 1e-15 coef = np.array([alpha, beta, gamma]) y = alpha + beta*x + gamma*x**2 print("roots") roots1(coef, x, y) print('Polynomial') roots2(coef, x, y) Outputs: roots [-1.e+15 0.e+00] [-1.e+15 0.e+00] [-1.00000000e+15 2.77555756e-17] [-1.00000000e+15 5.55111512e-17] Polynomial [-1.e+15 0.e+00] [-1.0e+15 2.5e-02] [-1.0e+15 -7.5e-02] [-1.e+15 -5.e-02] Best Regards, Cédric Hannotier

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NumPy Community Meeting Wednesday, July 17
by Sebastian Berg 30 Jul '19

30 Jul '19

Hi all, There will be a NumPy Community meeting Wednesday July 31 at 11 am Pacific Time. Everyone is invited to join in and edit the work-in- progress meeting topics and notes: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg Best wishes Sebastian

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Adding generic dtypes
by Charles R Harris 29 Jul '19

29 Jul '19

Hi All, The possibility of disabling default creation of object arrays has come up again. I'm wondering if one way to get there is to allow generic dtypes. The `numpy/core/numerictypes.py` module defines a hierarchy, and if we could allow things like `dtype=integer` or `dtype=no_object` and such we would gain more control over the resulting array types. At some point we could make `no_object` the default if we so desired. Thoughts? Chuck

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ENH: 2D polynomial fit
by Ansgar Wehrhahn 29 Jul '19

29 Jul '19

Hi, I made a function for 2d polynomial fits that I would like to contribute to numpy, as it is something that is not yet covered by numpy. I already made a pull request (https://github.com/numpy/numpy/pull/14151), but new functions need to be discussed. Any comments? Ansgar När du har kontakt med oss på Uppsala universitet med e-post så innebär det att vi behandlar dina personuppgifter. För att läsa mer om hur vi gör det kan du läsa här: http://www.uu.se/om-uu/dataskydd-personuppgifter/ E-mailing Uppsala University means that we will process your personal data. For more information on how this is performed, please read here: http://www.uu.se/en/about-uu/data-protection-policy

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NumPy 1.17.0rc1 released
by Charles R Harris 26 Jul '19

26 Jul '19

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.17.0rc1. The 1.17 release contains a number of new features that should substantially improve its performance and usefulness. The Python versions supported are 3.5-3.7, note that Python 2.7 has been dropped. Python 3.8b1 should work with the released source packages, but there are no guarantees about future releases. Highlights of this release are: - A new extensible random module along with four selectable random numbe5 generators and improved seeding designed for use in parallel processes has been added. The currently available bit generators are MT19937, PCG64, Philox, and SFC64. - NumPy's FFT implementation was changed from fftpack to pocketfft, resulting in faster, more accurate transforms and better handling of datasets of prime length. - New radix sort and timsort sorting methods. It is currently not possible to choose which will be used, but they are hardwired to the datatype and used when either ``stable`` or ``mergesort`` is passed as the method. - Overriding numpy functions is now possible by default Downstream developers should use Cython >= 0.29.10 for Python 3.8 support and OpenBLAS >= 3.7 (not currently out) to avoid problems on the Skylake architecture. The NumPy wheels on PyPI are built from the OpenBLAS development branch in order to avoid those problems. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.17.0rc1/>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.17.0rc1>. *Contributors* A total of 142 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Aaron Voelker + - Abdur Rehman + - Abdur-Rahmaan Janhangeer + - Abhinav Sagar + - Adam J. Stewart + - Adam Orr + - Albert Thomas + - Alex Watt + - Alexander Blinne + - Alexander Shadchin - Allan Haldane - Ander Ustarroz + - Andras Deak - Andreas Schwab - Andrew Naguib + - Andy Scholand + - Ankit Shukla + - Anthony Sottile - Antoine Pitrou - Antony Lee - Arcesio Castaneda Medina + - Assem + - Bernardt Duvenhage + - Bharat Raghunathan + - Bharat123rox + - Bran + - Bruce Merry + - Charles Harris - Chirag Nighut + - Christoph Gohlke - Christopher Whelan + - Chuanzhu Xu + - Daniel Hrisca - Daniel Lawrence + - Debsankha Manik + - Dennis Zollo + - Dieter Werthmüller + - Dominic Jack + - EelcoPeacs + - Eric Larson - Eric Wieser - Fabrice Fontaine + - Gary Gurlaskie + - Gregory Lee + - Gregory R. Lee - Hameer Abbasi - Haoyu Sun + - He Jia + - Hunter Damron + - Ian Sanders + - Ilja + - Isaac Virshup + - Isaiah Norton + - Jaime Fernandez - Jakub Wilk - Jan S. (Milania1) + - Jarrod Millman - Javier Dehesa + - Jeremy Lay + - Jim Turner + - Jingbei Li + - Joachim Hereth + - John Belmonte + - John Kirkham - John Law + - Jonas Jensen - Joseph Fox-Rabinovitz - Joseph Martinot-Lagarde - Josh Wilson - Juan Luis Cano Rodríguez - Julian Taylor - Jérémie du Boisberranger + - Kai Striega + - Katharine Hyatt + - Kevin Sheppard - Kexuan Sun - Kiko Correoso + - Kriti Singh + - Lars Grueter + - Maksim Shabunin + - Manvi07 + - Mark Harfouche - Marten van Kerkwijk - Martin Reinecke + - Matthew Brett - Matthias Bussonnier - Matti Picus - Michel Fruchart + - Mike Lui + - Mike Taves + - Min ho Kim + - Mircea Akos Bruma - Nick Minkyu Lee - Nick Papior - Nick R. Papior + - Nicola Soranzo + - Nimish Telang + - OBATA Akio + - Oleksandr Pavlyk - Ori Broda + - Paul Ivanov - Pauli Virtanen - Peter Andreas Entschev + - Peter Bell + - Pierre de Buyl - Piyush Jaipuriayar + - Prithvi MK + - Raghuveer Devulapalli + - Ralf Gommers - Richard Harris + - Rishabh Chakrabarti + - Riya Sharma + - Robert Kern - Roman Yurchak - Ryan Levy + - Sebastian Berg - Sergei Lebedev + - Shekhar Prasad Rajak + - Stefan van der Walt - Stephan Hoyer - SuryaChand P + - Søren Rasmussen + - Thibault Hallouin + - Thomas A Caswell - Tobias Uelwer + - Tony LaTorre + - Toshiki Kataoka - Tyler Moncur + - Tyler Reddy - Valentin Haenel - Vrinda Narayan + - Warren Weckesser - Weitang Li - Wojtek Ruszczewski - Yu Feng - Yu Kobayashi + - Yury Kirienko + - @aashuli + - @euronion + - @luzpaz - @parul + - @spacescientist + Cheers, Charles Harris

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Creating a sine wave with exponential decay
by Ram Rachum 26 Jul '19

26 Jul '19

Hi everyone! Total Numpy newbie here. I'd like to create an array with a million numbers, that has a sine wave with exponential decay on the amplitude. In other words, I want the value of each cell n to be sin(n) * 2 ** (-n * factor). What would be the most efficient way to do that? Someone suggested I do something like this: y = np.sin(x) * np.exp(newfactor * x) But this would create 2 arrays, wouldn't it? Isn't that wasteful? Does Numpy provide an efficient way of doing that without creating a redundant array? Thanks for your help, Ram Rachum.

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NumPy-Discussion July 2019