Mailman 3 February 2016 - NumPy-Discussion

Re: [Numpy-discussion] making "low" optional in numpy.randint
by G Young Feb. 17, 2016

Feb. 17, 2016

Hello all, I have a PR open here <https://github.com/numpy/numpy/pull/7151> that makes "low" an optional parameter in numpy.randint and introduces new behavior into the API as follows: 1) `low == None` and `high == None` Numbers are generated over the range `[lowbnd, highbnd)`, where `lowbnd = np.iinfo(dtype).min`, and `highbnd = np.iinfo(dtype).max`, where `dtype` is the provided integral type. 2) `low != None` and `high == None` If `low >= 0`, numbers are <b>still</b… [View More]

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Re: [Numpy-discussion] Fwd: Multi-distribution Linux wheels - please test
by Matthew Brett Feb. 17, 2016

Feb. 17, 2016

Hi, On Tue, Feb 9, 2016 at 12:01 PM, Freddy Rietdijk <freddyrietdijk(a)fridh.nl> wrote: > On Nix we also had trouble with OpenBLAS 0.2.15. Version 0.2.14 did not > cause any segmentation faults so we reverted to that version. > https://github.com/scipy/scipy/issues/5620 > > (hopefully this time the e-mail gets through) In hope, I tried building with 0.2.12 and 0.2.14, but 0.2.12 gave me an extra test failure, and 0.2.14 gave the same test failure as 0.2.15.. Sadly... Matthew

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NumPy 1.11.0b3 released.
by Charles R Harris Feb. 17, 2016

Feb. 17, 2016

Hi All, I'm pleased to announce the release of NumPy 1.11.0b3. This beta contains additional bug fixes as well as limiting the number of FutureWarnings raised by assignment to masked array slices. One issue that remains to be decided is whether or not to postpone raising an error for floats used as indexes. Sources may be found on Sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.11.0b3/> and both sources and OS X wheels are availble on pypi. Please test, hopefully this … [View More]

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building NumPy with gcc if Python was built with icc?!?
by BERGER Christian Feb. 17, 2016

Feb. 17, 2016

Hi All, Here's a potentially dumb question: is it possible to build NumPy with gcc, if python was built with icc? Right now, the build is failing in the toolchain check phase, because gcc doesn't know how to handle icc-specific c flags (like -fp-model, prec-sqrt, ...) In our environment we're providing an embedded python that our customers should be able to use and extend with 3rd party modules (like numpy). Problem is that our sw is built using icc, but we don't want to force our customers to … [View More]

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Re: [Numpy-discussion] Fwd: Windows wheels for testing
by R Schumacher Feb. 16, 2016

Feb. 16, 2016

At 03:45 PM 2/12/2016, you wrote: >PS C:\tmp> c:\Python35\python -m venv np-testing >PS C:\tmp> .\np-testing\Scripts\Activate.ps1 >(np-testing) PS C:\tmp> pip install -f >https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds numpy nose C:\Python34\Scripts>pip install "D:\Python distros\numpy-1.10.4-cp34-none-win_amd64.whl" Unpacking d:\python distros\numpy-1.10.4-cp34-none-win_amd64.whl Installing collected packages: numpy Successfully installed numpy Cleaning … [View More]up... C:\Python34\Scripts>..\python Python 3.4.2 (v3.4.2:ab2c023a9432, Oct 6 2014, 22:16:31) [MSC v.1600 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy >>> numpy.test() Running unit tests for numpy NumPy version 1.10.4 NumPy relaxed strides checking option: False NumPy is installed in C:\Python34\lib\site-packages\numpy Python version 3.4.2 (v3.4.2:ab2c023a9432, Oct 6 2014, 22:16:31) [MSC v.1600 64 bit (AMD64)] nose version 1.3.7 .......................F....S........................................................................................... .....................................................................................................S.................. ..........................................................................................C:\Python34\lib\unittest\case. py:162: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future callable_obj(*args, **kwargs) ........C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future callable_obj(*args, **kwargs) C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a non-integer number instead of an integer will result i n an error in the future callable_obj(*args, **kwargs) .......................................................................................S................................ ........................................................................................................................ ..........................................................................C:\Python34\lib\unittest\case.py:162: Deprecat ionWarning: using a non-integer number instead of an integer will result in an error in the future callable_obj(*args, **kwargs) ..C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future callable_obj(*args, **kwargs) C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a non-integer number instead of an integer will result i n an error in the future callable_obj(*args, **kwargs) C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a non-integer number instead of an integer will result i n an error in the future callable_obj(*args, **kwargs) C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a non-integer number instead of an integer will result i n an error in the future callable_obj(*args, **kwargs) ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ 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...............................................K.........................................................C:\Python34\lib \site-packages\numpy\ma\core.py:989: RuntimeWarning: invalid value encountered in multiply masked_da = umath.multiply(m, da) C:\Python34\lib\site-packages\numpy\ma\core.py:989: RuntimeWarning: invalid value encountered in multiply masked_da = umath.multiply(m, da) ........................................................................................................................ ..................C:\Python34\lib\site-packages\numpy\core\tests\test_numerictypes.py:372: DeprecationWarning: using a n on-integer number instead of an integer will result in an error in the future return self.ary['f0', 'f1'] ........................................................................................................................ .............................................................................................K.......................... ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ ....K..K................................K...SK.S.......S................................................................ ..................ES..SS................................................................................................ ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ 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...........................................S............................................................................ ..........................................S............................................................C:\Python34\lib\s ite-packages\numpy\ma\core.py:4089: UserWarning: Warning: converting a masked element to nan. warnings.warn("Warning: converting a masked element to nan.") ..............................................................................................................C:\Python3 4\lib\site-packages\numpy\ma\core.py:5116: RuntimeWarning: invalid value encountered in power np.power(out, 0.5, out=out, casting='unsafe') ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ .......................... ====================================================================== ERROR: test_compile1 (test_system_info.TestSystemInfoReading) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python34\lib\site-packages\numpy\distutils\tests\test_system_info.py", line 182, in test_compile1 c.compile([os.path.basename(self._src1)], output_dir=self._dir1) File "C:\Python34\lib\distutils\msvc9compiler.py", line 460, in compile self.initialize() File "C:\Python34\lib\site-packages\numpy\distutils\msvccompiler.py", line 17, in initialize distutils.msvccompiler.MSVCCompiler.initialize(self, plat_name) File "C:\Python34\lib\distutils\msvc9compiler.py", line 371, in initialize vc_env = query_vcvarsall(VERSION, plat_spec) File "C:\Python34\lib\distutils\msvc9compiler.py", line 259, in query_vcvarsall raise DistutilsPlatformError("Unable to find vcvarsall.bat") distutils.errors.DistutilsPlatformError: Unable to find vcvarsall.bat ====================================================================== FAIL: test_blasdot.test_blasdot_used ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python34\lib\site-packages\nose\case.py", line 198, in runTest self.test(*self.arg) File "C:\Python34\lib\site-packages\numpy\testing\decorators.py", line 146, in skipper_func return f(*args, **kwargs) File "C:\Python34\lib\site-packages\numpy\core\tests\test_blasdot.py", line 31, in test_blasdot_used assert_(dot is _dotblas.dot) File "C:\Python34\lib\site-packages\numpy\testing\utils.py", line 53, in assert_ raise AssertionError(smsg) AssertionError ---------------------------------------------------------------------- Ran 5575 tests in 32.042s FAILED (KNOWNFAIL=8, SKIP=12, errors=1, failures=1) <nose.result.TextTestResult run=5575 errors=1 failures=1> >>> [View Less]

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Proposal to add `weights` to `np.percentile` and `np.median`
by Joseph Fox-Rabinovitz Feb. 16, 2016

Feb. 16, 2016

I would like to add a `weights` keyword to `np.partition`, `np.percentile` and `np.median`. My reason for doing so is to to allow `np.histogram` to process automatic bin selection with weights. Currently, weights are not supported for the automatic bin selection and would be difficult to support in `auto` mode without having `np.percentile` support a `weights` keyword. I suspect that there are many other uses for such a feature. I have taken a preliminary look at the C implementation of the … [View More]

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Re: [Numpy-discussion] [Suggestion] Labelled Array
by Sérgio Feb. 16, 2016

Feb. 16, 2016

Just something I tried with pandas: >>> image array([[[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19]], [[20, 21, 22, 23, 24], [25, 26, 27, 28, 29], [30, 31, 32, 33, 34], [35, 36, 37, 38, 39]], [[40, 41, 42, 43, 44], [45, 46, 47, 48, 49], [50, 51, 52, 53, 54], [55, 56, 57, 58, 59]]]) >>> label array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 5], [2,… [View More] 3, 4, 5, 6], [3, 4, 5, 6, 7]]) >>> dt = pd.DataFrame(np.vstack((label.ravel(), image.reshape(3, 20))).T) >>> labelled_image = dt.groupby(0) >>> labelled_image.mean().values array([[ 0, 20, 40], [ 3, 23, 43], [ 6, 26, 46], [ 9, 29, 49], [10, 30, 50], [13, 33, 53], [16, 36, 56], [19, 39, 59]]) Sergio > Date: Sat, 13 Feb 2016 22:41:13 -0500 > From: Allan Haldane <allanhaldane(a)gmail.com> > To: numpy-discussion(a)scipy.org > Subject: Re: [Numpy-discussion] [Suggestion] Labelled Array > Message-ID: <56BFF759.7010505(a)gmail.com> > Content-Type: text/plain; charset=windows-1252; format=flowed > > Impressive! > > Possibly there's still a case for including a 'groupby' function in > numpy itself since it's a generally useful operation, but I do see less > of a need given the nice pandas functionality. > > At least, next time someone asks a stackoverflow question like the ones > below someone should tell them to use pandas! > > (copied from my gist for future list reference). > > http://stackoverflow.com/questions/4373631/sum-array-by-number-in-numpy > > http://stackoverflow.com/questions/31483912/split-numpy-array-according-to-… > > http://stackoverflow.com/questions/31863083/python-split-numpy-array-based-… > > http://stackoverflow.com/questions/28599405/splitting-an-array-into-two-sma… > > http://stackoverflow.com/questions/7662458/how-to-split-an-array-according-… > > Allan > > > On 02/13/2016 01:39 PM, Jeff Reback wrote: > > In [10]: pd.options.display.max_rows=10 > > > > In [13]: np.random.seed(1234) > > > > In [14]: c = np.random.randint(0,32,size=100000) > > > > In [15]: v = np.arange(100000) > > > > In [16]: df = DataFrame({'v' : v, 'c' : c}) > > > > In [17]: df > > Out[17]: > > c v > > 0 15 0 > > 1 19 1 > > 2 6 2 > > 3 21 3 > > 4 12 4 > > ... .. ... > > 99995 7 99995 > > 99996 2 99996 > > 99997 27 99997 > > 99998 28 99998 > > 99999 7 99999 > > > > [100000 rows x 2 columns] > > > > In [19]: df.groupby('c').count() > > Out[19]: > > v > > c > > 0 3136 > > 1 3229 > > 2 3093 > > 3 3121 > > 4 3041 > > .. ... > > 27 3128 > > 28 3063 > > 29 3147 > > 30 3073 > > 31 3090 > > > > [32 rows x 1 columns] > > > > In [20]: %timeit df.groupby('c').count() > > 100 loops, best of 3: 2 ms per loop > > > > In [21]: %timeit df.groupby('c').mean() > > 100 loops, best of 3: 2.39 ms per loop > > > > In [22]: df.groupby('c').mean() > > Out[22]: > > v > > c > > 0 49883.384885 > > 1 50233.692165 > > 2 48634.116069 > > 3 50811.743992 > > 4 50505.368629 > > .. ... > > 27 49715.349425 > > 28 50363.501469 > > 29 50485.395933 > > 30 50190.155223 > > 31 50691.041748 > > > > [32 rows x 1 columns] > > > > > > On Sat, Feb 13, 2016 at 1:29 PM, <josef.pktd(a)gmail.com > > <mailto:josef.pktd@gmail.com>> wrote: > > > > > > > > On Sat, Feb 13, 2016 at 1:01 PM, Allan Haldane > > <allanhaldane(a)gmail.com <mailto:allanhaldane@gmail.com>> wrote: > > > > Sorry, to reply to myself here, but looking at it with fresh > > eyes maybe the performance of the naive version isn't too bad. > > Here's a comparison of the naive vs a better implementation: > > > > def split_classes_naive(c, v): > > return [v[c == u] for u in unique(c)] > > > > def split_classes(c, v): > > perm = c.argsort() > > csrt = c[perm] > > div = where(csrt[1:] != csrt[:-1])[0] + 1 > > return [v[x] for x in split(perm, div)] > > > > >>> c = randint(0,32,size=100000) > > >>> v = arange(100000) > > >>> %timeit split_classes_naive(c,v) > > 100 loops, best of 3: 8.4 ms per loop > > >>> %timeit split_classes(c,v) > > 100 loops, best of 3: 4.79 ms per loop > > > > > > The usecases I recently started to target for similar things is 1 > > Million or more rows and 10000 uniques in the labels. > > The second version should be faster for large number of uniques, I > > guess. > > > > Overall numpy is falling far behind pandas in terms of simple > > groupby operations. bincount and histogram (IIRC) worked for some > > cases but are rather limited. > > > > reduce_at looks nice for cases where it applies. > > > > In contrast to the full sized labels in the original post, I only > > know of applications where the labels are 1-D corresponding to rows > > or columns. > > > > Josef > > > > > > In any case, maybe it is useful to Sergio or others. > > > > Allan > > > > > > On 02/13/2016 12:11 PM, Allan Haldane wrote: > > > > I've had a pretty similar idea for a new indexing function > > 'split_classes' which would help in your case, which > > essentially does > > > > def split_classes(c, v): > > return [v[c == u] for u in unique(c)] > > > > Your example could be coded as > > > > >>> [sum(c) for c in split_classes(label, data)] > > [9, 12, 15] > > > > I feel I've come across the need for such a function often > > enough that > > it might be generally useful to people as part of numpy. The > > implementation of split_classes above has pretty poor > > performance > > because it creates many temporary boolean arrays, so my plan > > for a PR > > was to have a speedy version of it that uses a single pass > > through v. > > (I often wanted to use this function on large datasets). > > > > If anyone has any comments on the idea (good idea. bad > > idea?) I'd love > > to hear. > > > > I have some further notes and examples here: > > https://gist.github.com/ahaldane/1e673d2fe6ffe0be4f21 > > > > Allan > > > > On 02/12/2016 09:40 AM, S?rgio wrote: > > > > 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 something like this > > efficiently without a > > loop. Just wondering... > > > > S?rgio. > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion(a)scipy.org > > <mailto:NumPy-Discussion@scipy.org> > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion(a)scipy.org <mailto:NumPy-Discussion@scipy.org> > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion(a)scipy.org <mailto:NumPy-Discussion@scipy.org> > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion(a)scipy.org > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > [View Less]

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Re: [Numpy-discussion] NumPy 1.11.0b3 released.
by josef.pktd＠gmail.com Feb. 16, 2016

Feb. 16, 2016

On Mon, Feb 15, 2016 at 10:46 PM, <josef.pktd(a)gmail.com> wrote: > > > On Fri, Feb 12, 2016 at 4:19 PM, Nathan Goldbaum <nathan12343(a)gmail.com> > wrote: > >> https://github.com/numpy/numpy/blob/master/doc/release/1.11.0-notes.rst >> >> On Fri, Feb 12, 2016 at 3:17 PM, Andreas Mueller <t3kcit(a)gmail.com> >> wrote: >> >>> Hi. >>> Where can I find the changelog? >>> It would be good for us to know which … [View More]changes are done one purpos >>> without hunting through the issue tracker. >>> >>> Thanks, >>> Andy >>> >>> >>> On 02/09/2016 09:09 PM, Charles R Harris wrote: >>> >>> Hi All, >>> >>> I'm pleased to announce the release of NumPy 1.11.0b3. This beta >>> contains additional bug fixes as well as limiting the number of >>> FutureWarnings raised by assignment to masked array slices. One issue that >>> remains to be decided is whether or not to postpone raising an error for >>> floats used as indexes. Sources may be found on Sourceforge >>> <https://sourceforge.net/projects/numpy/files/NumPy/1.11.0b3/> and both >>> sources and OS X wheels are availble on pypi. Please test, hopefully this >>> will be that last beta needed. >>> >>> As a note on problems encountered, twine uploads continue to fail for >>> me, but there are still variations to try. The wheeluploader downloaded >>> wheels as it should, but could not upload them, giving the error message >>> "HTTPError: 413 Client Error: Request Entity Too Large for url: >>> <https://www.python.org/pypi>https://www.python.org/pypi". Firefox also >>> complains that http://wheels.scipy.org is incorrectly configured with >>> an invalid certificate. >>> >>> Enjoy, >>> >>> Chuck >>> >>> >>> _______________________________________________ >>> NumPy-Discussion mailing listNumPy-Discussion@scipy.orghttps://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion(a)scipy.org >>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion(a)scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > (try to send again) > > another indexing question: (not covered by unit test but showed up in > examples in statsmodels) > > > This works in numpy at least 1.9.2 and 1.6.1 (python 2.7, and python 3.4) > > >>> list(range(5))[np.array([0])] > 0 > > > > on numpy 0.11.0b2 (I'm not yet at b3) (python 3.4) > > I get the same exception as here but even if there is just one element > > > >>> list(range(5))[np.array([0, 1])] > Traceback (most recent call last): > File "<pyshell#7>", line 1, in <module> > list(range(5))[np.array([0, 1])] > TypeError: only integer arrays with one element can be converted to an > index > > > the actual code uses pop on a python list with a return from > np.where(...)[0] that returns a one element int64 array > > Josef > [View Less]

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Fwd: Numexpr-3.0 proposal
by Robert McLeod Feb. 16, 2016

Feb. 16, 2016

On Mon, Feb 15, 2016 at 10:43 AM, Gregor Thalhammer < gregor.thalhammer(a)gmail.com> wrote: > > Dear Robert, > > thanks for your effort on improving numexpr. Indeed, vectorized math > libraries (VML) can give a large boost in performance (~5x), except for a > couple of basic operations (add, mul, div), which current compilers are > able to vectorize automatically. With recent gcc even more functions are > vectorized, see https://sourceware.org/glibc/wiki/libmvec … [View More]But you need > special flags depending on the platform (SSE, AVX present?), runtime > detection of processor capabilities would be nice for distributing > binaries. Some time ago, since I lost access to Intels MKL, I patched > numexpr to use Accelerate/Veclib on os x, which is preinstalled on each > mac, see https://github.com/geggo/numexpr.git veclib_support branch. > > As you increased the opcode size, I could imagine providing a bit to > switch (during runtime) between internal functions and vectorized ones, > that would be handy for tests and benchmarks. > Dear Gregor, Your suggestion to separate the opcode signature from the library used to execute it is very clever. Based on your suggestion, I think that the natural evolution of the opcodes is to specify them by function signature and library, using a two-level dict, i.e. numexpr.interpreter.opcodes['exp_f8f8f8'][gnu] = some_enum numexpr.interpreter.opcodes['exp_f8f8f8'][msvc] = some_enum +1 numexpr.interpreter.opcodes['exp_f8f8f8'][vml] = some_enum + 2 numexpr.interpreter.opcodes['exp_f8f8f8'][yeppp] = some_enum +3 I want to procedurally generate opcodes.cpp and interpreter_body.cpp. If I do it the way you suggested funccodes.hpp and all the many #define's regarding function codes in the interpreter can hopefully be removed and hence simplify the overall codebase. One could potentially take it a step further and plan (optimize) each expression, similar to what FFTW does with regards to matrix shape. That is, the basic way to control the library would be with a singleton library argument, i.e.: result = ne.evaluate( "A*log(foo**2 / bar**2", lib=vml ) However, we could also permit a tuple to be passed in, where each element of the tuple reflects the library to use for each operation in the AST tree: result = ne.evaluate( "A*log(foo**2 / bar**2", lib=(gnu,gnu,gnu,yeppp,gnu) ) In this case the ops are (mul,mul,div,log,mul). The op-code picking is done by the Python side, and this tuple could be potentially optimized by numexpr rather than hand-optimized, by trying various permutations of the linked C math libraries. The wisdom from the planning could be pickled and saved in a wisdom file. Currently Numexpr has cacheDict in util.py but there's no reason this can't be pickled and saved to disk. I've done a similar thing by creating wrappers for PyFFTW already. Robert -- Robert McLeod, Ph.D. Center for Cellular Imaging and Nano Analytics (C-CINA) Biozentrum der Universität Basel Mattenstrasse 26, 4058 Basel Work: +41.061.387.3225 robert.mcleod(a)unibas.ch robert.mcleod(a)bsse.ethz.ch <robert.mcleod(a)ethz.ch> robbmcleod(a)gmail.com [View Less]

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Numexpr-3.0 proposal
by Robert McLeod Feb. 16, 2016

Feb. 16, 2016

Hello everyone, I've done some work on making a new version of Numexpr that would fix some of the limitations of the original virtual machine with regards to data types and operation/function count. Basically I re-wrote the Python and C sides to use 4-byte words, instead of null-terminated strings, for operations and passing types. This means the number of operations and types isn't significantly limited anymore. Francesc Alted suggested I should come here and get some advice from the … [View More]

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cycler v.0.10.0 released
by Thomas Caswell Feb. 16, 2016

Feb. 16, 2016

Folks, I am happy to announce the next release of Cycler. This will become the minimal version for the upcoming mpl v2.0 release. http://matplotlib.org/cycler/ Feature release for `cycler`. This release includes a number of new features: - `Cycler` objecst learned to generate an `itertools.cycle` by calling them, a-la a generator. - `Cycler` objects learned to change the name of a key via the new `.change_key(old_key, new_key)` method. - `Cycler` objects learned how to compare each … [View More]

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Subclassing ma.masked_array, code broken after version 1.9
by Gutenkunst, Ryan N - (rgutenk) Feb. 15, 2016

Feb. 15, 2016

Hello all, In 2009 I developed an application that uses a subclass of masked arrays as a central data object. My subclass Spectrum possesses additional attributes along with many custom methods. It was very convenient to be able to use standard numpy functions for doing arithmetic on these objects. However, my code broke with numpy 1.10. I've finally had a chance to track down the problem, and I am hoping someone can suggest a workaround. See below for an example, which is as minimal as I … [View More]could concoct. In this case, I have a Spectrum object that I'd like to take the logarithm of using numpy.ma.log, while preserving the value of the "folded" attribute. Up to numpy 1.9, this worked as expected, but in numpy 1.10 and 1.11 the attribute is not preserved. The change in behavior appears to be driven by a commit made on Jun 16th, 2015 by Marten van Kerkwijk. In particular, the commit changed _MaskedUnaryOperation.__call__ so that the result array's update_from method is no longer called with the input array as the argument, but rather the result of the numpy UnaryOperation (old line 889, new line 885). Because that UnaryOperation doesn't carry my new attribute, it's not present for update_from to access. I notice that similar changes were made to MaskedBinaryOperation, although I haven't tested those. It's not clear to me from the commit message why this particular change was made, so I don't know whether this new behavior is intentional. I know that subclassing arrays isn't widely encouraged, but it has been very convenient in my code. Is it still possible to subclass masked_array in such a way that functions like numpy.ma.log preserve additional attributes? If so, can someone point me in the right direction? Thanks! Ryan *** Begin example import numpy print 'Working with numpy {0}'.format(numpy.__version__) class Spectrum(numpy.ma.masked_array): def __new__(cls, data, mask=numpy.ma.nomask, data_folded=None): subarr = numpy.ma.masked_array(data, mask=mask, keep_mask=True, shrink=True) subarr = subarr.view(cls) subarr.folded = data_folded return subarr def __array_finalize__(self, obj): if obj is None: return numpy.ma.masked_array.__array_finalize__(self, obj) self.folded = getattr(obj, 'folded', 'unspecified') def _update_from(self, obj): print('Input to update_from: {0}'.format(repr(obj))) numpy.ma.masked_array._update_from(self, obj) self.folded = getattr(obj, 'folded', 'unspecified') def __repr__(self): return 'Spectrum(%s, folded=%s)'\ % (str(self), str(self.folded)) fs1 = Spectrum([2,3,4.], data_folded=True) fs2 = numpy.ma.log(fs1) print('fs2.folded status: {0}'.format(fs2.folded)) print('Expectation is True, achieved with numpy 1.9') *** End example -- Ryan Gutenkunst Assistant Professor Molecular and Cellular Biology University of Arizona phone: (520) 626-0569, office LSS 325 http://gutengroup.mcb.arizona.edu Latest paper: "Computationally efficient composite likelihood statistics for demographic inference" Molecular Biology and Evolution; http://dx.doi.org/10.1093/molbev/msv255 [View Less]

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ANN: pandas v0.18.0rc1 - RELEASE CANDIDATE
by Jeff Reback Feb. 15, 2016

Feb. 15, 2016

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 … [View More]

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Modulus (remainder) function corner cases
by Charles R Harris Feb. 14, 2016

Feb. 14, 2016

Hi All, I'm curious as to what folks think about some choices in the compution of the remainder function. As an example where different choices can be made In [2]: -1e-64 % 1. Out[2]: 1.0 In [3]: float64(-1e-64) % 1. Out[3]: 0.99999999999999989 The first is Python, the second is in my branch. The first is more accurate numerically, but the modulus is of the same magnitude as the divisor. The second maintains the convention that the result must have smaller magnitude than the divisor. There … [View More]

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ANN: numpydoc 0.6.0 released
by Ralf Gommers Feb. 14, 2016

Feb. 14, 2016

Hi all, I'm pleased to announce the release of numpydoc 0.6.0. The main new feature is support for the Yields section in numpy-style docstrings. This is described in https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt Numpydoc can be installed from PyPi: https://pypi.python.org/pypi/numpydoc Cheers, Ralf

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Re: [Numpy-discussion] Fwd: Windows wheels for testing
by R Schumacher Feb. 13, 2016

Feb. 13, 2016

Have you all conferred with C Gohlke on his Windows build bot? I've never seen a description of his recipes. The MKL linking aside, his binaries always seem to work flawlessly. - Ray At 11:16 PM 2/12/2016, you wrote: >AFAIK the vcvarsall.bat error occurs when your >MSVC directories aren't properly linked in your >system registry, so Python cannot find the >file.Â This is not a numpy-specific issue, so I >certainly would agree that that failure is not blocking. > >… [View More]Other than that, this build contains the >mingw32.lib bug that I fixedÂ ><https://github.com/numpy/numpy/pull/7119>here, >but other than that, everything else passes on >relevant Python versions for 32-bit! > >On Sat, Feb 13, 2016 at 4:23 AM, Matthew Brett ><<mailto:matthew.brett@gmail.com>matthew.brett(a)gmail.com> wrote: >On Fri, Feb 12, 2016 at 8:18 PM, R Schumacher ><<mailto:rays@blue-cove.com>rays(a)blue-cove.com> wrote: > > At 03:45 PM 2/12/2016, you wrote: > >> > >> PS C:\tmp> c:\Python35\python -m venv np-testing > >> PS C:\tmp> .\np-testing\Scripts\Activate.ps1 > >> (np-testing) PS C:\tmp> pip install -f > >> > <https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds>https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds > numpy nose > > > > > > C:\Python34\Scripts>pip installÂ "D:\Python > > distros\numpy-1.10.4-cp34-none-win_amd64.whl" > > Unpacking d:\python distros\numpy-1.10.4-cp34-none-win_amd64.whl > > Installing collected packages: numpy > > Successfully installed numpy > > Cleaning up... > > > > C:\Python34\Scripts>..\python > > Python 3.4.2 (v3.4.2:ab2c023a9432, OctÂ 6 > 2014, 22:16:31) [MSC v.1600 64 bit > > (AMD64)] on win32 > > Type "help", "copyright", "credits" or "license" for more information. > >>>> import numpy > >>>> numpy.test() > > Running unit tests for numpy > > NumPy version 1.10.4 > > NumPy relaxed strides checking option: False > > NumPy is installed in C:\Python34\lib\site-packages\numpy > > Python version 3.4.2 (v3.4.2:ab2c023a9432, OctÂ 6 2014, 22:16:31) [MSC > > v.1600 64 bit (AMD64)] > > nose version 1.3.7 > > > .......................F....S........................................................................................... > > > .....................................................................................................S.................. > > > ..........................................................................................C:\Python34\lib\unittest\case. > > py:162: DeprecationWarning: using a > non-integer number instead of an integer > > will result in an error in the future > >Â Â callable_obj(*args, **kwargs) > > ........C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a > > non-integer number instead of an integer will > > result in an error in the future > >Â Â callable_obj(*args, **kwargs) > > C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a > > non-integer number instead of an integer will result i > > n an error in the future > >Â Â callable_obj(*args, **kwargs) > > > .......................................................................................S................................ > > > ........................................................................................................................ > > > ..........................................................................C:\Python34\lib\unittest\case.py:162: > > Deprecat > > ionWarning: using a non-integer number instead of an integer will result in > > an error in the future > >Â Â callable_obj(*args, **kwargs) > > ..C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a > > non-integer number instead of an integer will result > >Â in an error in the future > >Â Â callable_obj(*args, **kwargs) > > C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a > > non-integer number instead of an integer will result i > > n an error in the future > >Â Â callable_obj(*args, **kwargs) > > C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a > > non-integer number instead of an integer will result i > > n an error in the future > >Â Â callable_obj(*args, **kwargs) > > C:\Python34\lib\unittest\case.py:162: DeprecationWarning: using a > > non-integer number instead of an integer will result i > > n an error in the future > >Â Â callable_obj(*args, **kwargs) > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ...............................................K.........................................................C:\Python34\lib > > \site-packages\numpy\ma\core.py:989: RuntimeWarning: invalid value > > encountered in multiply > >Â Â masked_da = umath.multiply(m, da) > > C:\Python34\lib\site-packages\numpy\ma\core.py:989: RuntimeWarning: invalid > > value encountered in multiply > >Â Â masked_da = umath.multiply(m, da) > > > ........................................................................................................................ > > > ..................C:\Python34\lib\site-packages\numpy\core\tests\test_numerictypes.py:372: > > DeprecationWarning: using a n > > on-integer number instead of an integer will result in an error in the > > future > >Â Â return self.ary['f0', 'f1'] > > > ........................................................................................................................ > > > .............................................................................................K.......................... > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ....K..K................................K...SK.S.......S................................................................ > > > ..................ES..SS................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ................................................................................S....................................... > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ...........................................K.................K.......................................................... > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ...........................................S............................................................................ > > > ..........................................S............................................................C:\Python34\lib\s > > ite-packages\numpy\ma\core.py:4089: UserWarning: Warning: converting a > > masked element to nan. > >Â Â warnings.warn("Warning: converting a masked element to nan.") > > > ..............................................................................................................C:\Python3 > > 4\lib\site-packages\numpy\ma\core.py:5116: RuntimeWarning: invalid value > > encountered in power > >Â Â np.power(out, 0.5, out=out, casting='unsafe') > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > > ........................................................................................................................ > > .......................... > > ====================================================================== > > ERROR: test_compile1 (test_system_info.TestSystemInfoReading) > > ---------------------------------------------------------------------- > > Traceback (most recent call last): > >Â Â File > > "C:\Python34\lib\site-packages\numpy\distutils\tests\test_system_info.py", > > line 182, in test_compile1 > >Â Â Â c.compile([os.path.basename(self._src1)], output_dir=self._dir1) > >Â Â File "C:\Python34\lib\distutils\msvc9compiler.py", line 460, in compile > >Â Â Â self.initialize() > >Â Â File > "C:\Python34\lib\site-packages\numpy\distutils\msvccompiler.py", line > > 17, in initialize > >Â Â Â distutils.msvccompiler.MSVCCompiler.initialize(self, plat_name) > >Â Â File > "C:\Python34\lib\distutils\msvc9compiler.py", line 371, in initialize > >Â Â Â vc_env = query_vcvarsall(VERSION, plat_spec) > >Â Â File "C:\Python34\lib\distutils\msvc9compiler.py", line 259, in > > query_vcvarsall > >Â Â Â raise DistutilsPlatformError("Unable to find vcvarsall.bat") > > distutils.errors.DistutilsPlatformError: Unable to find vcvarsall.bat > > > > ====================================================================== > > FAIL: test_blasdot.test_blasdot_used > > ---------------------------------------------------------------------- > > Traceback (most recent call last): > >Â Â File "C:\Python34\lib\site-packages\nose\case.py", line 198, in runTest > >Â Â Â self.test(*self.arg) > >Â Â File "C:\Python34\lib\site-packages\numpy\testing\decorators.py", line > > 146, in skipper_func > >Â Â Â return f(*args, **kwargs) > >Â Â File "C:\Python34\lib\site-packages\numpy\core\tests\test_blasdot.py", > > line 31, in test_blasdot_used > >Â Â Â assert_(dot is _dotblas.dot) > >Â Â File > "C:\Python34\lib\site-packages\numpy\testing\utils.py", line 53, in > > assert_ > >Â Â Â raise AssertionError(smsg) > > AssertionError > > > > ---------------------------------------------------------------------- > > Ran 5575 tests in 32.042s > > > > FAILED (KNOWNFAIL=8, SKIP=12, errors=1, failures=1) > > <nose.result.TextTestResult run=5575 errors=1 failures=1> > >Great - thanks - I got the same couple of failures - I believe they >are benign... > >Matthew >_______________________________________________ >NumPy-Discussion mailing list ><mailto:NumPy-Discussion@scipy.org>NumPy-Discussion(a)scipy.org >https://mail.scipy.org/mailman/listinfo/numpy-discussion > > >_______________________________________________ >NumPy-Discussion mailing list >NumPy-Discussion(a)scipy.org >https://mail.scipy.org/mailman/listinfo/numpy-discussion [View Less]

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Fwd: Windows wheels for testing
by Matthew Brett Feb. 12, 2016

Feb. 12, 2016

On Fri, Feb 12, 2016 at 3:15 PM, R Schumacher <rays(a)blue-cove.com> wrote: > At 03:06 PM 2/12/2016, you wrote: > >> Any feedback would be very useful, > > > Sure, here's a little: > > C:\Python34\Scripts>pip install -f > https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds numpy > Requirement already satisfied (use --upgrade to upgrade): numpy in > c:\python34\lib\site-packages > Cleaning up... Thanks - yes - I should maybe have said that … [View More]testing in a virtual environment is your best bet. Here's me testing from Powershell on a 32-bit Windows and on Pythons 3.5 and 2.7: PS C:\tmp> c:\Python35\python -m venv np-testing PS C:\tmp> .\np-testing\Scripts\Activate.ps1 (np-testing) PS C:\tmp> pip install -f https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds numpy nose Collecting numpy Downloading https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds/numpy-1.10.4-cp… (6.6MB) 100% |################################| 6.6MB 34kB/s Collecting nose Using cached nose-1.3.7-py3-none-any.whl Installing collected packages: numpy, nose Successfully installed nose-1.3.7 numpy-1.10.4 You are using pip version 7.1.2, however version 8.0.2 is available. You should consider upgrading via the 'python -m pip install --upgrade pip' command. (np-testing) PS C:\tmp> python -c 'import numpy; numpy.test()' Running unit tests for numpy NumPy version 1.10.4 NumPy relaxed strides checking option: False NumPy is installed in C:\tmp\np-testing\lib\site-packages\numpy Python version 3.5.0 (v3.5.0:374f501f4567, Sep 13 2015, 02:16:59) [MSC v.1900 32 bit (Intel)] nose version 1.3.7 ......... [test output] Now on 2.7 (np-testing) PS C:\tmp> deactivate PS C:\tmp> C:\Python27\Scripts\pip.exe install virtualenv [...] output snipped PS C:\tmp> C:\Python27\Scripts\virtualenv.exe np27-testing Ignoring indexes: https://pypi.python.org/simple Collecting pip Collecting wheel Collecting setuptools Installing collected packages: pip, wheel, setuptools Successfully installed pip-7.1.2 setuptools-18.5 wheel-0.26.0 PS C:\tmp> .\np27-testing\Scripts\activate.ps1 (np27-testing) PS C:\tmp> pip install -f https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds numpy nose Collecting numpy c:\tmp\np27-testing\lib\site-packages\pip\_vendor\requests\packages\urllib3\util\ssl_.py:90: InsecurePlatformWarning: A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatf ormwarning. InsecurePlatformWarning Downloading https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds/numpy-1.10.4-cp… (6.4MB) 100% |################################| 6.4MB 35kB/s Collecting nose c:\tmp\np27-testing\lib\site-packages\pip\_vendor\requests\packages\urllib3\util\ssl_.py:90: InsecurePlatformWarning: A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatf ormwarning. InsecurePlatformWarning Using cached nose-1.3.7-py2-none-any.whl Installing collected packages: numpy, nose Successfully installed nose-1.3.7 numpy-1.10.4 You are using pip version 7.1.2, however version 8.0.2 is available. You should consider upgrading via the 'python -m pip install --upgrade pip' command. (np27-testing) PS C:\tmp> python -c 'import numpy; numpy.test()' Running unit tests for numpy NumPy version 1.10.4 NumPy relaxed strides checking option: False NumPy is installed in c:\tmp\np27-testing\lib\site-packages\numpy Python version 2.7.8 (default, Jun 30 2014, 16:03:49) [MSC v.1500 32 bit (Intel)] nose version 1.3.7 .............. [etc] Thanks a lot for testing, Matthew [View Less]

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Re: [Numpy-discussion] Windows wheels for testing
by R Schumacher Feb. 12, 2016

Feb. 12, 2016

At 03:06 PM 2/12/2016, you wrote: >Any feedback would be very useful, Sure, here's a little: C:\Python34\Scripts>pip install -f https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds numpy Requirement already satisfied (use --upgrade to upgrade): numpy in c:\python34\lib\site-packages Cleaning up...

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Windows wheels for testing
by Matthew Brett Feb. 12, 2016

Feb. 12, 2016

Hi, We're talking about putting up Windows wheels for numpy on pypi - here: https://github.com/numpy/numpy/issues/5479 I've built some wheels that might be suitable - available here: http://nipy.bic.berkeley.edu/scipy_installers/atlas_builds/ I'd be very grateful if y'all would test these. They should install with something like: pip install -f https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds numpy This should work for Pythons 2.7, 3.4, 3.5, both 32 and 64-bit. Any feedback … [View More]

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Hook in __init__.py to let distributors patch numpy
by Matthew Brett Feb. 12, 2016

Feb. 12, 2016

Hi, Over at https://github.com/numpy/numpy/issues/5479 we're discussing Windows wheels. On thing that we would like to be able to ship Windows wheels, is to be able to put some custom checks into numpy when you build the wheels. Specifically, for Windows, we're building on top of ATLAS BLAS / LAPACK, and we need to check that the system on which the wheel is running, has SSE2 instructions, otherwise we know ATLAS will crash (almost everybody does have SSE2 these days). The way I propose we … [View More]

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Deprecating `numpy.iterable`
by Joseph Fox-Rabinovitz Feb. 11, 2016

Feb. 11, 2016

I have created a PR to deprecate `np.iterable` (https://github.com/numpy/numpy/pull/7202). It is a very old function, introduced as a utility in 2005 (https://github.com/numpy/numpy/commit/052a7b2e3276a303be1083022fc24d43084d2…), and there is no good reason for it to be part of the public API. It is used internally 10 times within numpy. I have repaced those usages with a private function `np.lib.function_base._iterable` and added a `DeprecationWarning` to the public function. Is there anyone … [View More]

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Build fortran extension on Windows with gfortran and MSVC
by Marek Wojciechowski Feb. 11, 2016

Feb. 11, 2016

Hi! It seems that on Windows + Python-3.5 fortran extensions cannot be built anymore with f2py and mingw32 compilers, because of new MSVC. Here is the short description of the errors one gets: http://stackoverflow.com/questions/33822554/build-fortran-extension-on-wind… This is sad, because this worked quite nicely with all previous python versions. I'm curious what are the options now? Is this going to be supported in the future, with mingw-w64 for example? Regards, -- Marek --- … [View More]

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Linking other libm-Implementation
by Nils Becker Feb. 10, 2016

Feb. 10, 2016

Hi all, I wanted to know if there is any sane way to build numpy while linking to a different implementation of libm? A drop-in replacement for libm (e.g. openlibm) should in principle work, I guess, but I did not manage to actually make it work. As far as I understand the build code, setting MATHLIB=openlibm should suffice, but it did not. The build works fine, but in the end when running numpy apparently the functions of the system libm.so are used. I could not verify this directly (as I do … [View More]

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Numpy 1.11.0b2 released
by Charles R Harris Feb. 10, 2016

Feb. 10, 2016

Hi All, I hope I am pleased to announce the Numpy 1.11.0b2 release. The first beta was a damp squib due to missing files in the released source files, this release fixes that. The new source filese may be downloaded from sourceforge, no binaries will be released until the mingw tool chain problems are sorted. Please test and report any problem. Chuck

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Fwd: Multi-distribution Linux wheels - please test
by Evgeni Burovski Feb. 9, 2016

Feb. 9, 2016

---------- Forwarded message ---------- From: Evgeni Burovski <evgeny.burovskiy(a)gmail.com> Date: Mon, Feb 8, 2016 at 11:56 AM Subject: Re: [Numpy-discussion] Multi-distribution Linux wheels - please test To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> On Sat, Feb 6, 2016 at 8:26 PM, Matthew Brett <matthew.brett(a)gmail.com> wrote: > Hi, > > As some of you may have seen, Robert McGibbon and Nathaniel have just > guided a PEP for multi-… [View More]distribution Linux wheels past the approval > process over on distutils-sig: > > https://www.python.org/dev/peps/pep-0513/ > > The PEP includes a docker image on which y'all can build wheels which > match the PEP: > > https://quay.io/repository/manylinux/manylinux > > Now we're at the stage where we need stress-testing of the built > wheels to find any problems we hadn't thought of. > > I've built numpy and scipy wheels here: > > https://nipy.bic.berkeley.edu/manylinux/ > > So, if you have a Linux distribution handy, we would love to hear from > you about the results of testing these guys, maybe on the lines of: > > pip install -f https://nipy.bic.berkeley.edu/manylinux numpy scipy > python -c 'import numpy; numpy.test()' > python -c 'import scipy; scipy.test()' > > These manylinux wheels should soon be available on pypi, and soon > after, installable with latest pip, so we would like to fix as many > problems as possible before going live. > > Cheers, > > Matthew > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion Hi, Bog-standard Ubuntu 12.04, fresh virtualenv: Python 2.7.3 (default, Jun 22 2015, 19:33:41) [GCC 4.6.3] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy >>> numpy.__version__ '1.10.4' >>> numpy.test() Running unit tests for numpy NumPy version 1.10.4 NumPy relaxed strides checking option: False NumPy is installed in /home/br/virtualenvs/manylinux/local/lib/python2.7/site-packages/numpy Python version 2.7.3 (default, Jun 22 2015, 19:33:41) [GCC 4.6.3] nose version 1.3.7 <snip> ====================================================================== ERROR: test_multiarray.TestNewBufferProtocol.test_relaxed_strides ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/br/virtualenvs/manylinux/local/lib/python2.7/site-packages/nose/case.py", line 197, in runTest self.test(*self.arg) File "/home/br/virtualenvs/manylinux/local/lib/python2.7/site-packages/numpy/core/tests/test_multiarray.py", line 5366, in test_relaxed_strides fd.write(c.data) TypeError: 'buffer' does not have the buffer interface ---------------------------------------------------------------------- * Scipy tests pass with one error in TestNanFuncs, but the interpreter crashes immediately afterwards. Same machine, python 3.5: both numpy and scipy tests pass. [View Less]

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