Mailman 3 March 2018 - 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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Where to discuss NEPs (was: Re: new NEP: np.AbstractArray and np.asabstractarray)
by Nathaniel Smith May 31, 2018

May 31, 2018

On Thu, Mar 8, 2018 at 7:06 AM, Marten van Kerkwijk <m.h.vankerkwijk(a)gmail.com> wrote: > Hi Nathaniel, > > Overall, hugely in favour! For detailed comments, it would be good to > have a link to a PR; could you put that up? Well, there's a PR here: https://github.com/numpy/numpy/pull/10706 But, this raises a question :-). (One which also came up here: https://github.com/numpy/numpy/pull/10704#issuecomment-371684170) There are sensible two workflows we could use (or at … [View More]

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PR to add an initializer kwarg to ufunc.reduce (and similar functions)
by Hameer Abbasi April 9, 2018

April 9, 2018

Hello, everyone. I’ve submitted a PR to add a initializer kwarg to ufunc.reduce. This is useful in a few cases, e.g., it allows one to supply a “default” value for identity-less ufunc reductions, and specify an initial value for reductions such as sum (other than zero.) Please feel free to review or leave feedback, (although I think Eric and Marten have picked it apart pretty well). https://github.com/numpy/numpy/pull/10635 Thanks, Hameer Sent from Astro <https://www.helloastro.com> for Mac

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ANN: numpydoc 0.8.0 release
by Ralf Gommers March 30, 2018

March 30, 2018

Hi all, I'm pleased to announce that a new release of numpydoc is available: - package: https://pypi.python.org/pypi/numpydoc - documentation (new in this release): https://numpydoc.readthedocs.io/en/latest/ This is a maintenance release with many small improvements. Likely your documentation will render with fewer warnings; e.g. for NumPy it removed ~300 irrelevant ones, while improving the rendered results. Thanks to everyone who contributed to this release! Cheers, Ralf

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NEUROLAB - SPYDER
by Fernando Fernandes Vieira March 30, 2018

March 30, 2018

Hello everyone How to install neurolab in spyder? Can someone help me Att.. _______________________________________________________ * FERNANDO FERNANDES VIEIRA* Departamento de Engenharia Sanitária e Ambiental - DESA Centro de Ciências e Tecnologia - CCT Universidade Estadual da Paraíba - UEPB Tel: (83) 3315-3333 (DESA) - (83) 98852-1461 (Pessoal) e-mail: fernando(a)uepb.edu.br (fernanvieira(a)gmail.com) Campina Grande - PB - Brasil _______________________________________________________

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best way of speeding up a filtering-like algorithm
by Moroney, Catherine M (398E) March 29, 2018

March 29, 2018

Hello, I have the following sample code (pretty simple algorithm that uses a rolling filter window) and am wondering what the best way is of speeding it up. I tried rewriting it in Cython by pre-declaring the variables but that didn’t buy me a lot of time. Then I rewrote it in Fortran (and compiled it with f2py) and now it’s lightning fast. But I would still like to know if I could rewrite it in pure python/numpy/scipy or in Cython and get a similar speedup. Here is the raw Python code: … [View More]def mixed_coastline_slow(nsidc, radius, count, mask=None): nsidc_copy = numpy.copy(nsidc) if (mask is None): idx_coastline = numpy.where(nsidc_copy == NSIDC_COASTLINE_MIXED) else: idx_coastline = numpy.where(mask & (nsidc_copy == NSIDC_COASTLINE_MIXED)) for (irow0, icol0) in zip(idx_coastline[0], idx_coastline[1]): rows = ( max(irow0-radius, 0), min(irow0+radius+1, nsidc_copy.shape[0]) ) cols = ( max(icol0-radius, 0), min(icol0+radius+1, nsidc_copy.shape[1]) ) window = nsidc[rows[0]:rows[1], cols[0]:cols[1]] npoints = numpy.where(window != NSIDC_COASTLINE_MIXED, True, False).sum() nsnowice = numpy.where( (window >= NSIDC_SEAICE_LOW) & (window <= NSIDC_FRESHSNOW), \ True, False).sum() if (100.0*nsnowice/npoints >= count): nsidc_copy[irow0, icol0] = MISR_SEAICE_THRESHOLD return nsidc_copy and here is my attempt at Cython-izing it: import numpy cimport numpy as cnumpy cimport cython cdef int NSIDC_SIZE = 721 cdef int NSIDC_NO_SNOW = 0 cdef int NSIDC_ALL_SNOW = 100 cdef int NSIDC_FRESHSNOW = 103 cdef int NSIDC_PERMSNOW = 101 cdef int NSIDC_SEAICE_LOW = 1 cdef int NSIDC_SEAICE_HIGH = 100 cdef int NSIDC_COASTLINE_MIXED = 252 cdef int NSIDC_SUSPECT_ICE = 253 cdef int MISR_SEAICE_THRESHOLD = 6 def mixed_coastline(cnumpy.ndarray[cnumpy.uint8_t, ndim=2] nsidc, int radius, int count): cdef int irow, icol, irow1, irow2, icol1, icol2, npoints, nsnowice cdef cnumpy.ndarray[cnumpy.uint8_t, ndim=2] nsidc2 \ = numpy.empty(shape=(NSIDC_SIZE, NSIDC_SIZE), dtype=numpy.uint8) cdef cnumpy.ndarray[cnumpy.uint8_t, ndim=2] window \ = numpy.empty(shape=(2*radius+1, 2*radius+1), dtype=numpy.uint8) nsidc2 = numpy.copy(nsidc) idx_coastline = numpy.where(nsidc2 == NSIDC_COASTLINE_MIXED) for (irow, icol) in zip(idx_coastline[0], idx_coastline[1]): irow1 = max(irow-radius, 0) irow2 = min(irow+radius+1, NSIDC_SIZE) icol1 = max(icol-radius, 0) icol2 = min(icol+radius+1, NSIDC_SIZE) window = nsidc[irow1:irow2, icol1:icol2] npoints = numpy.where(window != NSIDC_COASTLINE_MIXED, True, False).sum() nsnowice = numpy.where( (window >= NSIDC_SEAICE_LOW) & (window <= NSIDC_FRESHSNOW), \ True, False).sum() if (100.0*nsnowice/npoints >= count): nsidc2[irow, icol] = MISR_SEAICE_THRESHOLD return nsidc2 Thanks in advance for any advice! Catherine [View Less]

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PR adding support for object arrays to np.isinf, np.isnan, np.isfinite
by Joseph Fox-Rabinovitz March 29, 2018

March 29, 2018

I have opened PR #10820 to add support for `dtype=object` to `np.isinf`, `np.isnan`, `np.isfinite`. The PR is a fairly minor change, but I would like to make sure that I understand at least the basics of ufuncs before I start adding support for datetimes and timedeltas to `np.isfinite` and eventually to `np.histogram`. I have left a few comments in areas I am not sure about, and would greatly appreciate feedback, even if the PR is not found suitable for merging. With this PR, object arrays … [View More]

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round(numpy.float64(0.0)) is a numpy.float64
by Olivier March 27, 2018

March 27, 2018

Hello, Is it normal, expected and desired that : round(numpy.float64(0.0)) is a numpy.float64 while round(numpy.float(0.0)) is an integer? I find it disturbing and misleading. What do you think? Has it already been discussed somewhere else? Best regards, Olivier

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