NumPy-Discussion August 2016

numpy-discussion@python.org

35 participants
21 discussions

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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Added atleast_nd, request for clarification/cleanup of atleast_3d
by Joseph Fox-Rabinovitz 27 Oct '16

27 Oct '16

Hi, I have generalized np.atleast_1d, np.atleast_2d, np.atleast_3d with a function np.atleast_nd in PR#7804 (https://github.com/numpy/numpy/pull/7804). As a result of this PR, I have a couple of questions about `np.atleast_3d`. `np.atleast_3d` appears to do something weird with the dimensions: If the input is 1D, it prepends and appends a size-1 dimension. If the input is 2D, it appends a size-1 dimension. This is inconsistent with `np.atleast_2d`, which always prepends (as does `np.atleast_nd`). - Is there any reason for this behavior? - Can it be cleaned up (e.g., by reimplementing `np.atleast_3d` in terms of `np.atleast_nd`, which is actually much simpler)? This would be a slight API change since the output would not be exactly the same. Thanks, -Joe

11 25

Numpy 1.11.2
by Charles R Harris 09 Sep '16

09 Sep '16

Hi All, I would like to release Numpy 1.11.2rc1 this weekend. It will contain a few small fixes and enhancements for windows and the last Scipy release. If there are any pending PRs that you think should go in or be backported for this release, please speak up. Chuck

2 4

Which NumPy/Numpy/numpy spelling?
by mail＠telenczuk.pl 08 Sep '16

08 Sep '16

Hi all, What is the official spelling of NumPy/Numpy/numpy? The documentation is not consistent and it mixes both NumPy and Numpy. For example, the reference manual uses both spellings in the introduction paragraph (http://docs.scipy.org/doc/numpy/reference/): "This reference manual details functions, modules, and objects included in Numpy, describing what they are and what they do. For learning how to use NumPy, see also NumPy User Guide." However, in all docs taken together "NumPy" is most frequently used (74%): % find . -name "*.rst" -exec grep Numpy -ow {} \; | wc -l 161 % find . -name "*.rst" -exec grep NumPy -ow {} \; | wc -l 471 I also reported it as an issue: https://github.com/numpy/numpy/issues/7986 Yours, Bartosz

8 9

Reading in a mesh file
by Florian Lindner 01 Sep '16

01 Sep '16

Hello, I have mesh (more exactly: just a bunch of nodes) description with values associated to the nodes in a file, e.g. for a 3x3 mesh: 0 0 10 0 0.3 11 0 0.6 12 0.3 0 20 0.3 0.3 21 0.3 0.6 22 0.6 0 30 0.6 0.3 31 0.6 0.6 32 What is best way to read it in and get data structures like the ones I get from np.meshgrid? Of course, I know about np.loadtxt, but I'm having trouble getting the resulting arrays (x, y, values) in the right form and to retain association to the values. Thanks, Florian

2 3

State-of-the-art to use a C/C++ library from Python
by Michael Bieri 01 Sep '16

01 Sep '16

Hi all There are several ways on how to use C/C++ code from Python with NumPy, as given in http://docs.scipy.org/doc/numpy/user/c-info.html . Furthermore, there's at least pybind11. I'm not quite sure which approach is state-of-the-art as of 2016. How would you do it if you had to make a C/C++ library available in Python right now? In my case, I have a C library with some scientific functions on matrices and vectors. You will typically call a few functions to configure the computation, then hand over some pointers to existing buffers containing vector data, then start the computation, and finally read back the data. The library also can use MPI to parallelize. Best regards, Michael

8 10

Include last element when subindexing numpy arrays?
by Matti Viljamaa 31 Aug '16

31 Aug '16

Is there a clean way to include the last element when subindexing numpy arrays? Since the default behaviour of numpy arrays is to omit the “stop index”. So for, >>> A array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> A[0:5] array([0, 1, 2, 3, 4])

2 3

Why np.fft.rfftfreq only returns up to Nyqvist?
by Matti Viljamaa 31 Aug '16

31 Aug '16

What’s the reasonability of np.fft.rfftfreq returning frequencies only up to Nyquist, rather than for the full sample rate?

2 1

Indexing issue with ndarrays
by Joseph Fox-Rabinovitz 26 Aug '16

26 Aug '16

This issue recently came up on Stack Overflow: http://stackoverflow.com/questions/39145795/masking-a-series-with-a-boolean…. The poster attempted to index an ndarray with a pandas boolean Series object (all False), but the result was as if he had indexed with an array of integer zeros. Can someone explain this behavior? I can see two obvious possibilities: 1. ndarray checks if the input to __getitem__ is of exactly the right type, not using instanceof. 2. pandas actually uses a wider datatype than boolean internally, so indexing with the series is in fact indexing with an integer array. In my attempt to reproduce the poster's results, I got the following warning: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index This indicates that the issue is probably #1 and that a fix is already on the way. Please correct me if I am wrong. Also, where does the code for ndarray.__getitem__ live? Thanks, -Joe

2 3

numpy.distutils issue
by Pavlyk, Oleksandr 25 Aug '16

25 Aug '16

Hi All, According to the documentation page: http://docs.scipy.org/doc/numpy/reference/distutils.html Function add_library allows the following keywords: extra_f77_compiler_args extra_f90_compiler_args however setting them seem to have no effect for my extension. Digging deeper, I discovered, the documentation is inconsistent with the implementation, as per https://github.com/numpy/numpy/blob/v1.11.0/numpy/distutils/fcompiler/__ini… https://github.com/numpy/numpy/blob/v1.11.0/numpy/distutils/fcompiler/__ini… And indeed, setting extra_f77_compile_arg has the effect I was looking for. Fixing it is easy, but I am less certain whether we should fix the docs, or the code. Given that add_extension lists extra_compile_args, extra_f77_compile_args, etc, I would think it Is the documentation that need to change. Please confirm, and I will open up a pull request for this. Thank you, Oleksandr

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NumPy-Discussion August 2016