Mailman 3 December 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 … [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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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, … [View More]

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FeatureRequest: support for array construction from iterators
by Stephan Sahm Feb. 18, 2016

Feb. 18, 2016

[ this request /discussion refers to numpy issue #5863 https://github.com/numpy/numpy/pull/5863#issuecomment-159738368 ] Dear all, As far as I can think, the expected functionality of np.array(...) would be np.array(list(...)) or something even nicer. Therefore, I like to request a generator/iterator support for np.array(...) as far as list(...) supports it. A more detailed reasoning behind this follows now. In general it seems possible to identify iterators/generators … [View More]

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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 … [View More]

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Proposal for a new function: np.moveaxis
by Stephan Hoyer Jan. 2, 2016

Jan. 2, 2016

I've put up a pull request implementing a new function, np.moveaxis, as an alternative to np.transpose and np.rollaxis: https://github.com/numpy/numpy/pull/6630 This functionality has been discussed (even the exact function name) several times over the years, but it never made it into a pull request. The most pressing issue is that the behavior of np.rollaxis is not intuitive to most users: https://mail.scipy.org/pipermail/numpy-discussion/2010-September/052882.html https://github.com/numpy/… [View More]

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Numpy funding update
by Ralf Gommers Jan. 2, 2016

Jan. 2, 2016

Hi all, A quick good news message: OSDC has made a $5k contribution to NumFOCUS, which is split between support for a women in technology workshop and support for Numpy: http://www.numfocus.org/blog/osdc-donates-5k-to-support-numpy-women-in-tech This was a very nice surprise to me, and a first sign that the FSA (fiscal sponsorship agreement) we recently signed with NumFOCUS is going to yield significant benefits for Numpy. NumFOCUS is also doing a special end-of-year fundraiser. Funds donated … [View More]

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what would you expect A[none] to do?
by Neal Becker Dec. 31, 2015

Dec. 31, 2015

In my case, what it does is: A.shape = (5760,) A[none] -> (1, 5760) In my case, use of none here is just a mistake. But why would you want this to be accepted at all, and how should it be interpreted?

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Dynamic array list implementation
by Nicolas P. Rougier Dec. 31, 2015

Dec. 31, 2015

I've coded a typed dynamic list based on numpy array (needed for the glumpy project). Code is available from https://github.com/rougier/numpy-list A Numpy array list is a strongly typed list whose type can be anything that can be interpreted as a numpy data type. >>> L = ArrayList( [[0], [1,2], [3,4,5], [6,7,8,9]] ) >>> print(L) [[0], [1 2], [3 4 5], [6 7 8 9]] >>> print(L.data) [0 1 2 3 4 5 6 7 8 9] You can add several items at once by specifying common or … [View More]

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random integers
by Charles R Harris Dec. 31, 2015

Dec. 31, 2015

Hi All, I've implemented several new random integer functions in #6910 <https://github.com/numpy/numpy/pull/6910>, to wit - np.random.random_int32 - np.random.random_int64 - np.random.random_intp These are the minimum functions that I think we need for the numpy 1.11.0 release, most especially the random_intp function for fuzz testing the mem_overlap functions. However, there is the question of the best way to expose the functions. Currently, they are all separately exposed, … [View More]

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How to find indices of values in an array (indirect in1d) ?
by Nicolas P. Rougier Dec. 30, 2015

Dec. 30, 2015

I’m scratching my head around a small problem but I can’t find a vectorized solution. I have 2 arrays A and B and I would like to get the indices (relative to B) of elements of A that are in B: >>> A = np.array([2,0,1,4]) >>> B = np.array([1,2,0]) >>> print (some_function(A,B)) [1,2,0] # A[0] == 2 is in B and 2 == B[1] -> 1 # A[1] == 0 is in B and 0 == B[2] -> 2 # A[2] == 1 is in B and 1 == B[0] -> 0 Any idea ? I tried numpy.in1d with no luck. Nicolas

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Re: [Numpy-discussion] How to find indices of values in an array (indirect in1d) ?
by Peter Creasey Dec. 30, 2015

Dec. 30, 2015

> > In the end, I?ve only the list comprehension to work as expected > > A = [0,0,1,3] > B = np.arange(8) > np.random.shuffle(B) > I = [list(B).index(item) for item in A if item in B] > > > But Mark's and Sebastian's methods do not seem to work... > The function you want is also in the open source astronomy package iccpy ( https://github.com/Lowingbn/iccpy ), which essentially does a variant of Sebastian’s code (which I also couldn’t quite get working), and … [View More]

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Support of @= in numpy?
by Hans Larsen Dec. 27, 2015

Dec. 27, 2015

I have a Python3.5.1 64bits on Windows 10 same bit-length! I want to knowing when and what version of numpy, that support @= ? I have a functional numpy-1-10-1!=-O -- Hans Larsen Galgebakken Sønder 4-11A 2620 Albertslund Danmark/Danio

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Problem writing array with savetxt (python3.5)
by Maxim Mayzel Dec. 26, 2015

Dec. 26, 2015

Dear all, I’m having a problem writing an array with np.savetxt, see below. (python3.5, numpy 1.10.1) import numpy as np a=np.ones(5,dtype=int) np.savetxt('t.txt',a) Works fine, but the content of ’t.txt’ is overwritten. But, passing file handle gives an error, while it works fine on python2.7 ! with open('t.txt','w') as t: np.savetxt(t,a,fmt='%i') ...: --------------------------------------------------------------------------- TypeError Traceback (… [View More]

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Re: [Numpy-discussion] [SciPy-Dev] ANN: first release candidate for scipy 0.17.0
by Matthew Brett Dec. 24, 2015

Dec. 24, 2015

Hi, On Wed, Dec 23, 2015 at 12:34 PM, Evgeni Burovski <evgeny.burovskiy(a)gmail.com> wrote: > Hi, > > I'm pleased to announce the availability of the first release > candidate for Scipy 0.17.0. Please try this rc and report any issues > on Github tracker or scipy-dev mailing list. > Source tarballs and full release notes are available from Github > Releases: https://github.com/scipy/scipy/releases/tag/v0.17.0rc1 > > Please note that this is a source-only … [View More]

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packaging of numpy for windows
by jo Dec. 23, 2015

Dec. 23, 2015

Hi I would like to package python + numpy and our app for windows using pynsist in such a way that the user does not need to compile anything. Although pynsist made a package numpy did not start after installation due to some missing libraries. Obviously including the numpy directory is not enough. Is there an nsi script or filelist for numpy what I should deliver ? Do I need msft redistributables? tx

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numpy-1.11.0.dev0 windows wheels compiled with mingwpy available
by Carl Kleffner Dec. 23, 2015

Dec. 23, 2015

I made numpy master (numpy-1.11.0.dev0 , https://github.com/numpy/numpy/commit/0243bce23383ff5e894b99e40df2f8fd806ad…) windows binary wheels available for testing. Install it with pip: > pip install -i https://pypi.anaconda.org/carlkl/simple numpy These builds are compiled with OPENBLAS trunk for BLAS/LAPACK support and the mingwpy compiler toolchain. OpenBLAS is deployed within the numpy wheels. To be performant on all usual CPU architectures OpenBLAS is configured with it's 'dynamic … [View More]

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Re: [Numpy-discussion] PR for complex np.interp, question about assert_almost_equal
by Peter Creasey Dec. 22, 2015

Dec. 22, 2015

>>> The tests pass on my machine, but I see that the TravisCI builds are >>> giving assertion fails (on my own test) with python 3.3 and 3.5 of the >>> form: >>> > assert_almost_equal >>> > TypeError: Cannot cast array data from dtype('complex128') to >>> dtype('float64') according to the rule 'safe' >>> > > The problem then is probably here > <https://github.com/numpy/numpy/pull/6872/files#diff-… [View More]

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Proposal: stop providing official win32 downloads (for now)
by Nathaniel Smith Dec. 22, 2015

Dec. 22, 2015

Hi all, I'm wondering what people think of the idea of us (= numpy) stopping providing our "official" win32 builds (the "superpack installers" distributed on sourceforge) starting with the next release. These builds are: - low quality: they're linked to an old & untuned build of ATLAS, so linear algebra will be dramatically slower than builds using MKL or OpenBLAS. They're win32 only and will never support win64. They're using an ancient version of gcc. They will never support python 3.5 … [View More]

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AppVeyor
by Charles R Harris Dec. 22, 2015

Dec. 22, 2015

Hi All, Just checking if someone has already registered numpy on appveyor. If not, I intend to rename my personal account. Note that as AFAICT, someone has to be the admin for appveyor, and that someone, like everyone else, can only have one account on appveyor. Other folks with accounts can be added as collaborators, but they cannot belong to the numpy account. Or something. Reading the appveyor documentation, such as it is, is like driving through a heavy fog and the user interface is … [View More]

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[ANN-JOB] Project Jupyter is hiring a Project Manager - position at UC Berkeley
by Fernando Perez Dec. 22, 2015

Dec. 22, 2015

Hi all, [ please direct all replies directly to me ] Project Jupyter is announcing the opening of a position for a full-time project manager, who will help us coordinate our technical development, engage the open source community and work with our multiple stakeholders in academia and industry. If you have experience leading technical teams in open source communities, we'd love to hear from you! In the last few years the project has rapidly grown in multiple directions, and this presents … [View More]

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Re: [Numpy-discussion] PR for complex np.interp, question about assert_almost_equal
by Peter Creasey Dec. 22, 2015

Dec. 22, 2015

>> > assert_almost_equal >> > TypeError: Cannot cast array data from dtype('complex128') to >> dtype('float64') according to the rule 'safe' >> >> > > Hi Peter, that error is unrelated to assert_almost_equal. What happens is > that when you pass in a complex argument `fp` to your modified > `compiled_interp`, you're somewhere doing a cast that's not safe and > trigger the error at > https://github.com/numpy/numpy/blob/master/numpy/core/src/… [View More]

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PR for complex np.interp, question about assert_almost_equal
by Peter Creasey Dec. 22, 2015

Dec. 22, 2015

Hi all, I submitted a PR (#6872) for using complex numbers in np.lib.interp. The tests pass on my machine, but I see that the TravisCI builds are giving assertion fails (on my own test) with python 3.3 and 3.5 of the form: > assert_almost_equal > TypeError: Cannot cast array data from dtype('complex128') to dtype('float64') according to the rule 'safe' When I was writing the test I used np.testing.assert_almost_equal with complex128 as it works in my python 2.7, however having checked … [View More]

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Why is masked string array fill value 'N/A'
by Charles R Harris Dec. 21, 2015

Dec. 21, 2015

Why, why, why is it not the empty string. you can't actually fill an `S2` type with such a string, hence one must convert masked string arrays to a list of strings to use it. That is pathetic. Chuck

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