Mailman 3 August 2016 - NumPy-Discussion

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

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numpy.distutils issue
by Pavlyk, Oleksandr 24 Aug '16

24 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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Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 119, Issue 11
by Dipankar “Dipu” Ganguly 21 Aug '16

21 Aug '16

Is there a way to use Wolframs’ Mathematica 11 within IPython on Jupyter running on Anaconda’s Navigator on a Mac with OS 10.11.6? Failing that, what Python package would give me those capabilities? Thanks. Dipu Dipankar Ganguly Consultant: Strategy/Technology/Commercialization Bothell, WA Cell: 408-203-8814 email: dipugee(a)gmail.com http://www.linkedin.com/in/dipugee > On Aug 21, 2016, at 5:00 AM, numpy-discussion-request(a)scipy.org wrote: > > Send NumPy-Discussion mailing list submissions to > numpy-discussion(a)scipy.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.scipy.org/mailman/listinfo/numpy-discussion > or, via email, send a message with subject or body 'help' to > numpy-discussion-request(a)scipy.org > > You can reach the person managing the list at > numpy-discussion-owner(a)scipy.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of NumPy-Discussion digest..." > > > Today's Topics: > > 1. coordinate bounds (Alan Isaac) > 2. Re: coordinate bounds (Robert Kern) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Sat, 20 Aug 2016 16:16:14 -0400 > From: Alan Isaac <alan.isaac(a)gmail.com> > To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> > Subject: [Numpy-discussion] coordinate bounds > Message-ID: <e58ac269-98c8-271b-22bf-1c73962ef935(a)gmail.com> > Content-Type: text/plain; charset=utf-8; format=flowed > > Is there a numpy equivalent to Mma's CoordinateBounds command? > http://reference.wolfram.com/language/ref/CoordinateBounds.html > > Thanks, > Alan Isaac > > > ------------------------------ > > Message: 2 > Date: Sat, 20 Aug 2016 22:14:38 +0100 > From: Robert Kern <robert.kern(a)gmail.com> > To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> > Subject: Re: [Numpy-discussion] coordinate bounds > Message-ID: > <CAF6FJiv+cRHM8H5QDhixiD56pHOgWRQTERLAmZmTwS1_zoOUzg(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > On Sat, Aug 20, 2016 at 9:16 PM, Alan Isaac <alan.isaac(a)gmail.com> wrote: >> >> Is there a numpy equivalent to Mma's CoordinateBounds command? >> http://reference.wolfram.com/language/ref/CoordinateBounds.html > > The first signature can be computed like so: > > np.transpose([coords.min(axis=0), coords.max(axis=0)]) > > -- > Robert Kern >

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coordinate bounds
by Alan Isaac 20 Aug '16

20 Aug '16

Is there a numpy equivalent to Mma's CoordinateBounds command? http://reference.wolfram.com/language/ref/CoordinateBounds.html Thanks, Alan Isaac

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How to trigger warnings for integer division in python 2
by Stuart Berg 19 Aug '16

19 Aug '16

Hi, To help people migrate their code bases from Python 2 to Python 3, the python interpreter has a handy option '-3' that issues warnings at runtime. One of the warnings is for integer division: $ echo "print 3/2" > /tmp/foo.py $ python -3 /tmp/foo.py /tmp/foo.py:1: DeprecationWarning: classic int division print 3/2 1 But no warnings are shown for division of numpy arrays, e.g. for a statement like this: print np.array([3]) / np.array([2]) I see that np.seterr can be used to issue certain types of division warnings, but not this one. Is there a way to activate integer division warnings? It would really help me migrate my application to Python 3. Thanks, Stuart

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Euroscipy
by Sebastian Berg 16 Aug '16

16 Aug '16

Hi all, I am still pondering whether or not (and if which days) to go to EuroScipy. Who else is there and would like to discuss a bit or whatever else? - Sebastian

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weighted random choice in Python
by Alan Isaac 16 Aug '16

16 Aug '16

It seems that there may soon be movement on this enhancement request: http://bugs.python.org/issue18844 The API is currently under discussion. If this decision might interact with NumPy in any way, it would be good to have that documented now. (See Raymond Hettinger's comment today.) Cheers, Alan Isaac

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NumPy in PyPy
by Papa, Florin 09 Aug '16

09 Aug '16

Hi, This is Florin Papa from the Dynamic Scripting Languages Optimizations team in Intel Corporation. Our team is working on optimizing the PyPy interpreter and part of this work is to find and fix incompatibilities between NumPy and PyPy. Does anyone have knowledge of real life workloads that use NumPy and cannot be run using PyPy? We are also interested in creating a repository with relevant benchmarks for real world usage of NumPy, like GUPB for CPython, but we have not found such workloads for NumPy. Thank you, Florin

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Views and Increments
by Anakim Border 08 Aug '16

08 Aug '16

Dear List, I'm experimenting with views and array indexing. I have written two code blocks that I was expecting to produce the same result. First try: >>> a = np.arange(10) >>> b = a[np.array([1,6,5])] >>> b += 1 >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) Alternative version: >>> a = np.arange(10) >>> a[np.array([1,6,5])] += 1 >>> a array([0, 2, 2, 3, 4, 6, 7, 7, 8, 9]) I understand what is happening in the first case. In fact, the documentation is quite clear on the subject: For all cases of index arrays, what is returned is a copy of the original data, not a view as one gets for slices. What about the second case? There, I'm not keeping a reference to the intermediate copy (b, in the first example). Still, I don't see why the update (to the copy) is propagating to the original array. Is there any implementation detail that I'm missing? Best, ab

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PR 7918 apply_along_axis()
by Ben Rowland 08 Aug '16

08 Aug '16

Hi list, This is both my first post to this list and first pull request for numpy so apologies if this is not the right list or for any other mistakes. Here is the link to my pull request: https://github.com/numpy/numpy/pull/7918 <https://github.com/numpy/numpy/pull/7918> It is a simple modification to the numpy.apply_along_axis() function in numpy/lib/shape_base.py to allow it to deal better with ndarray subclasses. My use case is this: I have an ndarray subclass storing sets of temporal data which has additional metadata properties such as the time between data points. I frequently want to apply the same function to each time course in a set and apply_along_axis() is a compact and efficient way to do this. However there are two behaviours that I require which are not provided by the current numpy master: 1) apply_along_axis returns the same subclass as it was called upon, so that I don’t lose all the metadata that it went in with. 2) fund1d calls inside apply_along_axis should receive 1d slices of the same subclass as the supplied whole array, so that they can make use of the metadata in performing their function. To achieve these two behaviours requires modifying only three lines of code: 1) At the start of the function, the input is converted to a bare ndarray by the function numpy.asarray(), I replace this with the subclass friendly numpy.asanyarray() 2) Depending on whether func1d returns a scalar or a vector there are two different code paths constructing and returning the output array. In each case I call __array_wrap__ on the input array, passing the output array to allow it to be updated with all the metadata of the original array. I have also implemented two new tests for this functionality. The first is very simple and merely calls apply_along_axis on a numpy.matrix class and checks that the result is also a numpy.matrix. The second is slightly more involved as it requires defining a MinimalSubclass class which adds a data member to the bare ndarray, then a function which returns that member. apply_along_axis() is then called passing in an instance of the subclass and the function to show that the slices passed to func1d preserve the data member. I would welcome any comment or constructive criticism. Ben Rowland

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