Mailman 3 February 2016 - 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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GSoC?
by Chris Barker March 6, 2016

March 6, 2016

ANyone interested in Google Summer of Code this year? I think the real challenge is having folks with the time to really put into mentoring, but if folks want to do it -- numpy could really benefit. Maybe as a python.org sub-project? https://wiki.python.org/moin/SummerOfCode/2016 Deadlines are approaching -- so I thought I'd ping the list and see if folks are interested. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) … [View More]

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Re: [Numpy-discussion] fromnumeric.py internal calls
by G Young Feb. 29, 2016

Feb. 29, 2016

Hello all, I have PR #7325 <https://github.com/numpy/numpy/pull/7325/files> up that changes the internal calls for functions in *fromnumeric.py* from positional arguments to keyword arguments. I made this change for two reasons: 1) It is consistent with the external function signature 2) The inconsistency caused a breakage in *pandas* in its own implementation of *searchsorted* in which the *sorter* argument is not really used but is accepted so as to make it easier for *numpy* users … [View More]

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Re: [Numpy-discussion] ANN: pandas v0.18.0rc1 - RELEASE CANDIDATE
by Jeff Feb. 28, 2016

Feb. 28, 2016

So you are probably reading sas7bdat, which was put in AFTER 0.18.0rc1 was cut (if you are reading xport format then you are good to go), otherwise you may want to wait a bit for 0.18.0rc2. On Sunday, February 28, 2016 at 1:42:53 PM UTC-5, John E wrote: > > OK, thanks, I got it. Although... I would consider pandas.pydata.org to > be a common end user gateway and if one starts there they will read "We > recommend that *all *users upgrade to this version." And then if they &… [View More]gt; scroll down a short distance they will see a single line instruction for > installing via conda: "conda install pandas=v0.18.0rc1 -c pandas". > > And also somewhat confusing to me about pandas.pydata.org is that looking > to the right, you have a choice of RC, dev, and previous releases, but > nothing that says something like "current, stable release". > > Anyways.... quite possibly this is confusing only to me, and not others, > but I thought I'd mention it just in case. FWIW. > > I've now installed 0.18.0rc1 and will try to test out some of the newer > features. I'm really interested to see how well the SAS reader works (i.e. > how fast). I hate SAS myself, but this would be a really, really nice > feature for my organization and likely increase adoption of python & pandas. > > > > On Sunday, February 28, 2016 at 12:03:45 PM UTC-5, Jeff wrote: >> >> >> These are pre-releases. In other words, we would want the community to >> test out before an official release, and see if there are any show >> stoppers. The docs are setup for the official releases. These are not put >> into official channels at all (that is the point), e.g. not on PyPi, nor in >> the conda main channels. Only official releases will go there. >> >> Generally we will try to do release candidates before major changes, but >> not before minor changes. >> >> So the official release of 0.18.0 has not happened yet! (in fact going to >> do a v0.18.0rc2 next week). >> >> We would love for you to test out! >> >> Jeff >> >> >> >> >> On Sunday, February 28, 2016 at 11:50:57 AM UTC-5, John E wrote: >>> >>> I hope this doesn't come across as a trivial, semantical question, but... >>> >>> The initial releases of the last 2 or so versions have been labelled as >>> "release candidates" but still say "We recommend that all >>> users upgrade to this version." >>> >>> So this is a little confusing to me for using pandas in a production >>> environment. "Release candidate" seems to suggest that you should wait for >>> 0.18.1, but the note unambiguously says not to wait. So which >>> interpretation is recommended for a production environment? >>> >>> >>> On Saturday, February 13, 2016 at 7:53:18 PM UTC-5, Jeff wrote: >>>> >>>> Hi, >>>> >>>> I'm pleased to announce the availability of the first release candidate >>>> of Pandas 0.18.0. >>>> Please try this RC and report any issues here: Pandas Issues >>>> <https://github.com/pydata/pandas/issues> >>>> We will be releasing officially in 1-2 weeks or so. >>>> >>>> **RELEASE CANDIDATE 1** >>>> >>>> This is a major release from 0.17.1 and includes a small number of API >>>> changes, several new features, >>>> enhancements, and performance improvements along with a large number of >>>> bug fixes. We recommend that all >>>> users upgrade to this version. >>>> >>>> Highlights include: >>>> >>>> - pandas >= 0.18.0 will no longer support compatibility with Python >>>> version 2.6 GH7718 <https://github.com/pydata/pandas/issues/7718> or >>>> version 3.3 GH11273 <https://github.com/pydata/pandas/issues/11273> >>>> - Moving and expanding window functions are now methods on Series >>>> and DataFrame similar to .groupby like objects, see here >>>> <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> >>>> . >>>> - Adding support for a RangeIndex as a specialized form of the >>>> Int64Index for memory savings, see here >>>> <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> >>>> . >>>> - API breaking .resample changes to make it more .groupby like, see >>>> here >>>> <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> >>>> - Removal of support for positional indexing with floats, which was >>>> deprecated since 0.14.0. This will now raise a TypeError, see here >>>> <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> >>>> - The .to_xarray() function has been added for compatibility with >>>> the xarray package <http://xarray.pydata.org/en/stable/> see here >>>> <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> >>>> . >>>> - Addition of the .str.extractall() method >>>> <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…>, >>>> and API changes to the the .str.extract() method >>>> <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…>, >>>> and the .str.cat() method >>>> <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> >>>> - pd.test() top-level nose test runner is available GH4327 >>>> <https://github.com/pydata/pandas/issues/4327> >>>> >>>> See the Whatsnew >>>> <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html> for >>>> much more information. >>>> >>>> Best way to get this is to install via conda >>>> <http://pandas-docs.github.io/pandas-docs-travis/install.html#installing-pan…> from >>>> our development channel. Builds for osx-64,linux-64,win-64 for Python 2.7 >>>> and Python 3.5 are all available. >>>> >>>> conda install pandas=v0.18.0rc1 -c pandas >>>> >>>> Thanks to all who made this release happen. It is a very large release! >>>> >>>> Jeff >>>> >>>> [View Less]

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Generalized flip function
by Eren Sezener Feb. 28, 2016

Feb. 28, 2016

Hi, In PR #7346 we add a flip function that generalizes fliplr and flipud for arbitrary axes. flipud and fliplr reverse the elements of an array along axis=0 and axis=1 respectively. The new flip function reverses the elements of an array along any given axis. In case flip is called with axis=0 or axis=1, the function is equivalent to flipud and fliplr respectively. A similar function is also available in MATLAB™. We use this function in PR #7347 to generalize the rot90 function to rotate … [View More]

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Re: [Numpy-discussion] ANN: pandas v0.18.0rc1 - RELEASE CANDIDATE
by Jeff Feb. 28, 2016

Feb. 28, 2016

These are pre-releases. In other words, we would want the community to test out before an official release, and see if there are any show stoppers. The docs are setup for the official releases. These are not put into official channels at all (that is the point), e.g. not on PyPi, nor in the conda main channels. Only official releases will go there. Generally we will try to do release candidates before major changes, but not before minor changes. So the official release of 0.18.0 has not … [View More]

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ANN: SciPy (Scientific Python) 2016 Conference Call for Talk / Tutorial Proposals Open Until 3/25
by Courtenay Godshall (Enthought) Feb. 26, 2016

Feb. 26, 2016

**SciPy 2016 Conference (Scientific Computing with Python) Announcement** *Call for Proposals: Submit Your Tutorial and Talk Ideas by March 25, 2015 at http://scipy2016.scipy.org. <http://www.scipy2016.scipy.org> SciPy 2016, the 15th annual Scientific Computing with Python conference, will be held July 11-17, 2016 in Austin, Texas. SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and … [View More]engineering. The annual SciPy Conference brings together over 650 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The full program will consist of <http://scipy2016.scipy.org/ehome/146062/332967/?&&> 2 days of tutorials (July 11-12), <http://scipy2016.scipy.org/ehome/146062/332968/?&&> 3 days of talks (July 13-15), and 2 days of <http://scipy2016.scipy.org/ehome/146062/332969/?&&> developer sprints (July 16-17). More info is available on the conference website at <http://www.scipy2016.scipy.org> http://scipy2016.scipy.org (where you can sign up for the mailing list); or follow <https://twitter.com/scipyconf> @scipyconf on Twitter. We hope you'll join us - early bird registration is open until May 22, 2016 at <http://scipy2016.scipy.org/ehome/146062/332936/?&&> http://scipy2016.scipy.org/ehome/146062/332936/?&& We encourage you to submit tutorial or talk proposals in the categories below; please also share with others who you'd like to see participate! Submit via the conference website: <http://www.scipy2016.scipy.org> http://scipy2016.scipy.org. ---------------------------------------------------------------------------- ------------------------- *SUBMIT A SCIPY 2016 TUTORIAL PROPOSAL - DUE MARCH 21, 2016* ---------------------------------------------------------------------------- ------------------------- Details and submission here: <http://scipy2016.scipy.org/ehome/146062/332967/?&&> http://scipy2016.scipy.org/ehome/146062/332967/?&& These sessions provide extremely affordable access to expert training, and consistently receive fantastic feedback from participants. We're looking for submissions on topics from introductory to advanced - we'll have attendees across the gamut looking to learn. Whether you are a major contributor to a scientific Python library or an expert-level user, this is a great opportunityto share your knowledge and stipends are available. ---------------------------------------------------------------------------- --------------------------------- **SUBMIT A SCIPY 2016 TALK / POSTER PROPOSAL - DUE MARCH 25, 2016* ---------------------------------------------------------------------------- --------------------------------- Details and submission here: <http://scipy2016.scipy.org/ehome/146062/332968/?&&> http://scipy2016.scipy.org/ehome/146062/332968/?&& SciPy 2016 will include 3 major topic tracks and 8 mini-symposia tracks. Major topic tracks include: - Scientific Computing in Python - Python in Data Science (Big data and not so big data) - High Performance Computing Mini-symposia will include the applications of Python in: - Earth and Space Science - Engineering - Medicine and Biology - Social Sciences - Special Purpose Databases - Case Studies in Industry - Education - Reproducibility If you have any questions or comments, feel free to contact us at: scipy-organizers(a)scipy.org ----------------------------------------------------------- **SCIPY 2016 REGISTRATION IS OPEN** ----------------------------------------------------------- Please register early. SciPy early bird registration until May 22, 2016! Register at <http://scipy2016.scipy.org> http://scipy2016.scipy.org. Plus, enter our t-shirt design contest to win a free registration. (Send a vector art file to scipy(a)enthought.com by March 31 to enter). [View Less]

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ANN: Stop plotting your data -- HoloViews 1.4 released!
by James A. Bednar Feb. 24, 2016

Feb. 24, 2016

We are pleased to announce the fifth public release of HoloViews, a Python package for exploring and visualizing numerical data: http://holoviews.org HoloViews provides composable, sliceable, declarative data structures for building even complex visualizations easily. Instead of you having to explicitly and laboriously plot your data, HoloViews lets you simply annotate your data so that any part of it visualizes itself automatically. You can now work with large datasets as easily as you … [View More]work with simple datatypes at the Python prompt. The new version can be installed using conda: conda install -c ioam holoviews Release 1.4 introduces major new features, incorporating over 1700 new commits and closing 142 issues: - Now supports both Bokeh (bokeh.pydata.org) and matplotlib backends, with Bokeh providing extensive interactive features such as panning and zooming linked axes, and customizable callbacks - DynamicMap: Allows exploring live streams from ongoing data collection or simulation, or parameter spaces too large to fit into your computer's or your browser's memory, from within a Jupyter notebook - Columnar data support: Underlying data storage can now be in Pandas dataframes, NumPy arrays, or Python dictionaries, allowing you to define HoloViews objects without copying or reformatting your data - New Element types: Area (area under or between curves), Spikes (sequence of lines, e.g. spectra, neural spikes, or rug plots), BoxWhisker (summary of a distribution), QuadMesh (nonuniform rasters), Trisurface (Delaunay-triangulated surface plots) - New Container type: GridMatrix (grid of heterogenous Elements) - Improved layout handling, with better support for varying aspect ratios and plot sizes - Improved help system, including recursively listing and searching the help for all the components of a composite object - Improved Jupyter/IPython notebook support, including improved export using nbconvert, and standalone HTML output that supports dynamic widgets even without a Python server - Significant performance improvements for large or highly nested data And of course we have fixed a number of bugs found by our very dedicated users; please keep filing Github issues if you find any! For the full list of changes, see: https://github.com/ioam/holoviews/releases HoloViews is now supported by Continuum Analytics, and is being used in a wide range of scientific and industrial projects. HoloViews remains freely available under a BSD license, is Python 2 and 3 compatible, and has minimal external dependencies, making it easy to integrate into your workflow. Try out the extensive tutorials at holoviews.org today! Jean-Luc R. Stevens Philipp Rudiger James A. Bednar Continuum Analytics, Inc., Austin, TX, USA School of Informatics, The University of Edinburgh, UK -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. [View Less]

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Numpy 1.11.0rc1 released.
by Charles R Harris Feb. 24, 2016

Feb. 24, 2016

Hi All, I'm delighted to announce the release of Numpy 1.11.0rc1. Hopefully the issues discovered in 1.11.0b3 have been dealt with and this release can go on to become the official release. Source files and documentation can be found on Sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.11.0rc1/>, while source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be installed from Pypi. Please test thoroughly. Chuck

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ANN: SfePy 2016.1
by Robert Cimrman Feb. 24, 2016

Feb. 24, 2016

I am pleased to announce release 2016.1 of SfePy. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by the finite element method or by the isogeometric analysis (preliminary support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: http://groups.google.com/group/sfepy-devel Git (source) repository, issue tracker, wiki: http://github.com/sfepy Highlights of this … [View More]

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ENH: `scale` parameter for `sinc`
by Joseph Fox-Rabinovitz Feb. 23, 2016

Feb. 23, 2016

I have created PR #7322 (https://github.com/numpy/numpy/pull/7322) to add a scale parameter to `sinc`. What this allows is to compute `sinc` as `sin(x)/x` or really `sin(n*x)/(n*x)` for arbitrary `n` instead of just `sin(pi*x)/(pi*x)` as is being done now. The parameter accepts two string arguments in addition to the actual scale value: 'normalized' and 'unnormalized'. 'normalized' is the default since that is the existing functionality. 'unnormalized' is equivalent to a `scale` of 1.0. The … [View More]

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How to check for memory leaks?
by Charles R Harris Feb. 23, 2016

Feb. 23, 2016

Hi All, I'm suspecting a possible memory leak in 1.11.x, what is the best way to check for that? Chuck

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reshaping empty array bug?
by Benjamin Root Feb. 23, 2016

Feb. 23, 2016

Not exactly sure if this should be a bug or not. This came up in a fairly general function of mine to process satellite data. Unexpectedly, one of the satellite files had no scans in it, triggering an exception when I tried to reshape the data from it. >>> import numpy as np >>> a = np.zeros((0, 5*64)) >>> a.shape (0, 320) >>> a.shape = (0, 5, 64) >>> a.shape (0, 5, 64) >>> a.shape = (0, 5*64) >>> a.shape = (0, 5, -1) Traceback (… [View More]

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DyND 0.7.1 Release
by Irwin Zaid Feb. 21, 2016

Feb. 21, 2016

Hello everyone, I'm pleased to announce the latest 0.7.1 release of DyND. The release notes are at https://github.com/libdynd/libdynd/blob/master/docs/release_notes.txt . Over the last 6 months, DyND has really matured a lot and many features that were "experimental" before are quite usable at the moment. At the same time, we still have bugs and issues to sort out, so I wouldn't claim DyND has reached stability just yet. Nevertheless, I'd encourage early adopters to try it out. You can … [View More]

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PyData Madrid
by Jaime Fernández del Río Feb. 20, 2016

Feb. 20, 2016

Hi all, I just found out there is a PyData Madrid happening in early April, and it would feel wrong not to go, it being my hometown and all. Aside from the usual "Who else is going? We should meet!" I was also thinking of submitting a proposal for a talk. My idea was to put something together on "The future of NumPy indexing" and use it as an opportunity to raise awareness and hopefully gather feedback from users on the proposed changes, in sort of a "if the mountain won't come to Muhammad" … [View More]

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[Suggestion] Labelled Array
by Sérgio Feb. 19, 2016

Feb. 19, 2016

Hello, This is my first e-mail, I will try to make the idea simple. Similar to masked array it would be interesting to use a label array to guide operations. Ex.: >>> x labelled_array(data = [[0 1 2] [3 4 5] [6 7 8]], label = [[0 1 2] [0 1 2] [0 1 2]]) >>> sum(x) array([9, 12, 15]) The operations would create a new axis for label indexing. You could think of it as a collection of masks, one for each label. I don't know a way to make … [View More]

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Re: [Numpy-discussion] proposal: new logspace without the log in the argument
by Joseph Fox-Rabinovitz Feb. 19, 2016

Feb. 19, 2016

If the author is willing, I'd say both functions are useful. The "geom" prefix is very fitting. - Joe ------ Original message------From: Robert KernDate: Fri, Feb 19, 2016 08:00To: Discussion of Numerical Python;Subject:Re: [Numpy-discussion] proposal: new logspace without the log in the argumentOn Fri, Feb 19, 2016 at 12:10 PM, Andrew Nelson <andyfaff(a)gmail.com> wrote: > > With respect to geomspace proposals: instead of specifying start and end values and the number of … [View More]

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Re: [Numpy-discussion] proposal: new logspace without the log in the argument
by Andrew Nelson Feb. 19, 2016

Feb. 19, 2016

With respect to geomspace proposals: instead of specifying start and end values and the number of points I'd like to have an option where I can set the start and end points and the ratio. The function would then work out the correct number of points to get closest to the end value. E.g. geomspace(start=1, finish=2, ratio=1.03) The first entries would be 1.0, 1.03, 1*1.03**2, etc. I have a requirement for the correct ratio between the points, and it's a right bind having to calculate the … [View More]

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Re: [Numpy-discussion] proposal: new logspace without the log in the argument
by Peter Creasey Feb. 18, 2016

Feb. 18, 2016

> > Some questions it'd be good to get feedback on: > > - any better ideas for naming it than "geomspace"? It's really too bad > that the 'logspace' name is already taken. > > - I guess the alternative interface might be something like > > np.linspace(start, stop, steps, spacing="log") > > what do people think? > > -n > You’ve got to wonder how many people actually use logspace(start, stop, num) in preference to 10.0**linspace(start, stop, num) - i.e. I … [View More]

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Suggestion: special-case np.array(range(...)) to be faster
by Antony Lee Feb. 18, 2016

Feb. 18, 2016

Compare (on Python3 -- for Python2, read "xrange" instead of "range"): In [2]: %timeit np.array(range(1000000), np.int64) 10 loops, best of 3: 156 ms per loop In [3]: %timeit np.arange(1000000, dtype=np.int64) 1000 loops, best of 3: 853 µs per loop Note that while iterating over a range is not very fast, it is still much better than the array creation: In [4]: from collections import deque In [5]: %timeit deque(range(1000000), 1) 10 loops, best of 3: 25.5 ms per loop On one hand, … [View More]

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proposal: new logspace without the log in the argument
by . Feb. 18, 2016

Feb. 18, 2016

I've suggested a new function similar to logspace, but where you specify the start and stop points directly instead of using log(start) and base arguments: https://github.com/numpy/numpy/issues/7255 https://github.com/numpy/numpy/pull/7268

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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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