Mailman 3 November 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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Reverse parameter in ordering functions
by mpro Dec. 24, 2019

Dec. 24, 2019

Hi all, I created pull request #14989, which adds reverse parameter in np.sort function. As suggested in the comments, I want to ask for some feedback on adding parameter 'reverse' . Not only in sort, but in ordering functions in general. Magdalena -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

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Updated numpy.random C-API
by Matti Picus Nov. 28, 2019

Nov. 28, 2019

Hi everyone, We recently officially exposed and documented[0] the C-API side of numpy.random. There are now working examples[3] of using numpy.random from numba[1], cython[2], and cffi[5]. Please try this out before the 1.18 release by installing the latest HEAD version and making sure the interfaces work for your use case. There is an open issue[4] for exploring such use cases, but we need feedback about where documentation is missing, where functionality falls short, and help with sample code demonstrating any of the use cases not yet explicitly tested. Matti [0] https://numpy.org/devdocs/reference/random/extending.html [1] https://numpy.org/devdocs/reference/random/extending.html#numba [2] https://numpy.org/devdocs/reference/random/extending.html#cython [3] https://numpy.org/devdocs/reference/random/extending.html#examples [4] https://github.com/numpy/numpy/issues/14778 [5] https://github.com/numpy/numpy/pull/14954

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NumPy community meeting Today: Wed Nov 27
by Sebastian Berg Nov. 27, 2019

Nov. 27, 2019

Hi all, There will be a NumPy Community meeting today (Wednesday Nov 27) 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?both Note that from now on, we may try to focus on more technical discussion (such as PR and issue triage) every second week. Best wishes Sebastian

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FLOSS for science podcast
by Patrick Diehl Nov. 26, 2019

Nov. 26, 2019

Dear Sir or Madam, I started with a colleague the podcast FLOSS for Science [0] with the goal of showcasing free, libre and open source software uses in science. We want to highlight how FLOSS empowers researchers and enables them to produce high quality research. Through each of our episodes, we want to showcase a scientist using FLOSS to produce his/her research or the developers of software used for scientific research. Would some one of you be interested to be interviewed about the numpy/scipy package. Best, Patrick [0] https://flossforscience.com/

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ANN: SciPy 1.3.3
by Tyler Reddy Nov. 23, 2019

Nov. 23, 2019

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA256 Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.3.3, a bug fix release that addresses a DLL loading issue for wheels and a multiprocessing test issue. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.3.3 One of a few ways to install this release with pip: pip install scipy==1.3.3 ===================== SciPy 1.3.3 Release Notes ===================== SciPy 1.3.3 is a bug-fix release with no new features compared to 1.3.2. In particular, a test suite issue involving multiprocessing was fixed for Windows and Python 3.8 on macOS. Wheels were also updated to place msvcp140.dll at the appropriate location, which was previously causing issues. Authors ======= Ilhan Polat Tyler Reddy Ralf Gommers Issues closed for 1.3.3 -------------------------------- * `#11033 <https://github.com/scipy/scipy/issues/11033>`__: deadlock on osx for python 3.8 Pull requests for 1.3.3 --------------------------------- * `#11034 <https://github.com/scipy/scipy/pull/11034>`__: MAINT: TST: Skip tests with multiprocessing that use "spawn" start method Checksums ========= MD5 ~~~ 69dabfe955c7092f5389a4374db57186 scipy-1.3.3-cp35-cp35m-macosx_10_6_intel.whl 23f3f5f48dc7893b9ff2a9a63adcf8c7 scipy-1.3.3-cp35-cp35m-manylinux1_i686.whl e80281d504225da4b081245ac44081b5 scipy-1.3.3-cp35-cp35m-manylinux1_x86_64.whl 7af194e334c13a9b13f8814f4e1695ce scipy-1.3.3-cp35-cp35m-win32.whl a39a6cb0436572f6149c87036288bd53 scipy-1.3.3-cp35-cp35m-win_amd64.whl e0b74a4370050d48175c5ef91c50dbeb scipy-1.3.3-cp36-cp36m-macosx_10_6_intel.whl 0bd21eb01dc34028d85fd2a9d752caf2 scipy-1.3.3-cp36-cp36m-manylinux1_i686.whl 64d9fb9b4ce5f9869834810dde4bc06f 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"Proposal to accept NEP 34: Disallow inferring dtype=object from sequence
by Matti Picus Nov. 23, 2019

Nov. 23, 2019

I propose to move the NEP https://numpy.org/neps/nep-0034.html from "draft" to "accepted" status. There were no objections (actually there were no responses at all) to the mail proposing the NEP https://mail.python.org/pipermail/numpy-discussion/2019-October/080200.html. PR 14794 https://github.com/numpy/numpy/pull/14794 is an implementation of the first step, deprecating current automatic detection, f there are no substantive objections within 7 days from this email, then the NEP will be accepted; see NEP 0 for more details. Matti

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PR 14966: Adding a new argument to np.asfarray
by Joseph Fox-Rabinovitz Nov. 23, 2019

Nov. 23, 2019

Hi, I've submitted PR #14966, which makes a couple of small, backward compatible, changes to the API of `asfarray`: 1. Added `copy` parameter that defaults to `False` 2. Added `None` option to the `dtype` parameter Item #1 is inspired by situations like the one in Stack Overflow question https://stackoverflow.com/q/58998475/2988730. Sometimes, you just need to ensure a copy, and it's nice not to have to check things like if `asfarray(x) is x: x = x.copy()`. Item #2 solves the problem of trying to do `asfarray(x, dtype=x.dtype)` for `x` that don't have a `dtype` attribute, like lists or tuples. I've made every effort to make `dtype` and `copy` play together nicely. On an unrelated note, I've also submitted #14967 to clean up the internals of `mintypecode` a little in the same file. Regards, - Joe

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