Mailman 3 July 2014 - 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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C-API: multidimensional array indexing?
by Johann Bauer 31 Mar '16

31 Mar '16

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, Google and mailing lists for quite a long time but didn't find anything. Can you help me? Thanks, Johann

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taking a 2D uneven surface slice
by Bryan Woods 04 Dec '14

04 Dec '14

I'm trying to do something that at first glance I think should be simple but I can't quite figure out how to do it. The problem is as follows: I have a 3D grid Values[Nx, Ny, Nz] I want to slice Values at a 2D surface in the Z dimension specified by Z_index[Nx, Ny] and return a 2D slice[Nx, Ny]. It is not as simple as Values[:,:,Z_index]. I tried this: >>> values.shape (4, 5, 6) >>> coords.shape (4, 5) >>> slice = values[:,:,coords] >>> slice.shape (4, 5, 4, 5) >>> slice = np.take(values, coords, axis=2) >>> slice.shape (4, 5, 4, 5) >>> Obviously I could create an empty 2D slice and then fill it by using np.ndenumerate to fill it point by point by selecting values[i, j, Z_index[i, j]]. This just seems too inefficient and not very pythonic.

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OSX wheels for older numpy versions on pypi
by Matthew Brett 01 Aug '14

01 Aug '14

Hi, I took the liberty of uploading OSX wheels for some older numpy versions to pypi. These can be useful for testing, or when building your own wheels to be compatible with earlier numpy versions - see: http://stackoverflow.com/questions/17709641/valueerror-numpy-dtype-has-the-… There are currently wheels for numpy 1.5.1 py27 numpy 1.6.0 py27 numpy 1.6.1 py27 numpy 1.7.1 py27, 32, 33, 34 These are all compiled against ATLAS: https://github.com/matthew-brett/numpy-atlas-binaries install with e.g. pip install numpy==1.6.1 If anyone needs other wheels compiled, let me know, I'll try and upload them, Cheers, Matthew

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working with numpy object arrays
by Moroney, Catherine M (398D) 01 Aug '14

01 Aug '14

In the example code below, is it possible to return an array of all the ".a" values of the MyClass objects as stored in the object array "a"? I am successfully able to retrieve the "a" attributes if I loop through the array elements one by one, but I cannot do a whole-array operation to retrieve the "a" attributes. Is there any way to retrieve all the "a" attributes of the MyClass objects all at once, or do I have to loop through all elements of "array" one-by-one? Thanks for any help, Catherine import numpy class MyClass(object): def __init__(self, a): self.a = a def add(self, b, c): self.a += b+c def return_a(self): return self.a array = numpy.empty((2,2), dtype=object) for i in xrange(0, 2): for j in xrange(0, 2): array[i,j] = MyClass(i+j) for i in xrange(0, 2): for j in xrange(0, 2): array[i,j].add(i, j) print "(%i,%i) = %i" % (i, j, array[i,j].a) try: array_a = array[:,:].a print "a values =",array_a except AttributeError: print "Unable to access a attributes of array as a whole." array_a = numpy.empty((2,2)) for i in xrange(0, 2): for j in xrange(0, 2): array_a[i,j] = array[i,j].a print "a values =",array_a

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ANN: NumPy 1.9.0 beta 2 release
by Julian Taylor 31 Jul '14

31 Jul '14

Hello, The source packages and binaries got numpy 1.9.0 beta 2 have just been uploaded to sourceforge. https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b2 1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2 - 3.4. Unfortunately we have disabled the new __numpy_ufunc__ feature for overriding ufuncs in subclasses for now. There are still some unresolved issues with its behavior regarding python operator precedence and subclasses. If you have a stake in the issue please read Pauli's summary of the remaining issues: http://mail.scipy.org/pipermail/numpy-discussion/2014-July/070737.html When the issues are resolved to everyones satisfaction we hope to enable the feature for 1.10 in its final form. We have restored the indexing edge case that broke matplotlib with numpy 1.9.0 beta 1 but some of the other test failures in other packages are deemed bugs in their code and not reasonable to support in numpy anymore. Most projects have fixed the issues in their latest stable or development versions. Depending on how bad the broken functionality is you may need to update your third party packages when updating numpy to 1.9.0b2. An attempt was made to update the windows binary toolchain to the latest mingw/mingw64 version and an up to date ATLAS version but this turned up a few ugly test failures. Help in resolving these issues is appreciated, no core developer has Windows debugging experience. Please see this issue for details: https://github.com/numpy/numpy/issues/4909 The changelog is mostly the same as in beta1. Please read it carefully there have been many small changes that could affect your code. https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-not… Please also take special note of the future changes section which will apply to the following release 1.10.0 and make sure to check if your applications would be affected by them. Source tarballs, windows installers and release notes can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b2 Cheers, Julian Taylor

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Remove user_array.py
by Charles R Harris 31 Jul '14

31 Jul '14

Hi All, numpy/lib/user_array.py is an old module (2006) that documents itself as unfinished. The only recent changes are my work for supporting both python2 and python3 from the same code base. It was apparently intended as an alternative to inheriting from ndarray. It has no tests to speak of except a few odds and ends included in the module. I suspect this is one of those features that few have heard of. Thoughts? Chuck

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Can't build numpy on my Windows 7 desktop computer
by Jeffrey Ken Smith 31 Jul '14

31 Jul '14

I have been unable to install on my Windows 7 desktop computer, which is a Dell - I had no problems installing it on my new laptop, which is also a Dell. When I try to run the superpack .exe file, I get a message claiming that Python2.7 is not in the registry even though it is and even though I was able to install pyodbc. If I download the zip file and try to use setup.py, I get messages like "No module named msvccompiler in numpy.distutils: trying from distutils error: unable to find vcvarsall.bat" I have no idea what this means or what to do about it.

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ANN: bcolz 0.7.1 released
by Francesc Alted 30 Jul '14

30 Jul '14

====================== Announcing bcolz 0.7.1 ====================== What's new ========== This is maintenance release, where bcolz got rid of the nose dependency for Python 2.6 (only unittest2 should be required). Also, some small fixes for the test suite, specially in 32-bit has been done. Thanks to Ilan Schnell for pointing out the problems and for suggesting fixes. ``bcolz`` is a renaming of the ``carray`` project. The new goals for the project are to create simple, yet flexible compressed containers, that can live either on-disk or in-memory, and with some high-performance iterators (like `iter()`, `where()`) for querying them. Together, bcolz and the Blosc compressor, are finally fullfilling the promise of accelerating memory I/O, at least for some real scenarios: http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/queryi… For more detailed info, see the release notes in: https://github.com/Blosc/bcolz/wiki/Release-Notes What it is ========== bcolz provides columnar and compressed data containers. Column storage allows for efficiently querying tables with a large number of columns. It also allows for cheap addition and removal of column. In addition, bcolz objects are compressed by default for reducing memory/disk I/O needs. The compression process is carried out internally by Blosc, a high-performance compressor that is optimized for binary data. bcolz can use numexpr internally so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too). numexpr optimizes the memory usage and use several cores for doing the computations, so it is blazing fast. Moreover, the carray/ctable containers can be disk-based, and it is possible to use them for seamlessly performing out-of-memory computations. bcolz has minimal dependencies (NumPy), comes with an exhaustive test suite and fully supports both 32-bit and 64-bit platforms. Also, it is typically tested on both UNIX and Windows operating systems. Installing ========== bcolz is in the PyPI repository, so installing it is easy: $ pip install -U bcolz Resources ========= Visit the main bcolz site repository at: http://github.com/Blosc/bcolz Manual: http://bcolz.blosc.org Home of Blosc compressor: http://blosc.org User's mail list: bcolz(a)googlegroups.com http://groups.google.com/group/bcolz License is the new BSD: https://github.com/Blosc/bcolz/blob/master/LICENSES/BCOLZ.txt ---- **Enjoy data!** -- Francesc Alted

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NumPy-Discussion July 2014