Mailman 3 February 2016 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker 11 Jul '23

11 Jul '23

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 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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GSoC?
by Chris Barker 06 Mar '16

06 Mar '16

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) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

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Re: [Numpy-discussion] fromnumeric.py internal calls
by G Young 29 Feb '16

29 Feb '16

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 who may be used to the *searchsorted* signature in *numpy*. The standard in *pandas* is to "swallow" those unused arguments into a *kwargs* argument so that we don't have to document an argument that we don't really use. However, that turned out not to be possible when *searchsorted* is called from the *numpy* library. Does anyone have any objections to the changes I made? Thanks! Greg

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Re: [Numpy-discussion] ANN: pandas v0.18.0rc1 - RELEASE CANDIDATE
by Jeff 28 Feb '16

28 Feb '16

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 > 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 >>>> >>>>

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Generalized flip function
by Eren Sezener 28 Feb '16

28 Feb '16

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 an arbitrary plane (defined by the axes argument) of a multidimensional array. By that we fix issue #6506. Because flip function introduces a new API, @shoyer asked us to consult the mailing list. Any objection to adding the generalized flip function? Best regards, C. Eren Sezener & Denis Alevi

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Re: [Numpy-discussion] ANN: pandas v0.18.0rc1 - RELEASE CANDIDATE
by Jeff 28 Feb '16

28 Feb '16

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 >> >>

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ANN: SciPy (Scientific Python) 2016 Conference Call for Talk / Tutorial Proposals Open Until 3/25
by Courtenay Godshall (Enthought) 26 Feb '16

26 Feb '16

**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 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).

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