Mailman 3 February 2016 - NumPy-Discussion

Re: [Numpy-discussion] proposal: new logspace without the log in the argument
by Andrew Nelson 19 Feb '16

19 Feb '16

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 exact number of points needed.

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Re: [Numpy-discussion] proposal: new logspace without the log in the argument
by Peter Creasey 18 Feb '16

18 Feb '16

> > 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 prefer the latter for clarity, and if I wanted performance I’d be prepared to write something more ugly. I don’t mind geomspace(), but if you are brainstorming >>> linlogspace(start, end) # i.e. ‘linear in log-space’ is ok for me too. Peter

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Suggestion: special-case np.array(range(...)) to be faster
by Antony Lee 18 Feb '16

18 Feb '16

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, special cases are awful. On the other hand, the range builtin is probably important enough to deserve a special case to make this construction faster. Or not? I initially opened this as https://github.com/numpy/numpy/issues/7233 but it was suggested there that this should be discussed on the ML first. (The real issue which prompted this suggestion: I was building sparse matrices using scipy.sparse.csc_matrix with some indices specified using range, and that construction step turned out to take a significant portion of the time because of the calls to np.array). Antony

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proposal: new logspace without the log in the argument
by . 18 Feb '16

18 Feb '16

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 18 Feb '16

18 Feb '16

[ 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 as needed for this purpose: - someone actually implemented this feature already (see #5863 <https://mail.google.com/mail/u/0/%E2%80%8Bhttps://github.com/numpy/numpy/pu…> ) - there is ``type.GeneratorType`` and ``collections.abc.Iterator`` for ``isinstance(...)`` check - numpy can destinguish them already from all other types which get well translated into a numpy array Given this, I think the general argument goes roughly like the following: PROS (effect maybe 10% of numpy user or more): - more intuitive overall behaviour, array(...) = array(list(...)) roughly - python3 compatibility (see e.g. #5951 <https://github.com/numpy/numpy/issues/5951>) - compatibility with analog ``__builtin__`` functions (see e.g. #5756 <https://github.com/numpy/numpy/issues/5756>) - all the above make numpy easier to use in an interactive style (e.g. ipython --pylab) (computation not that important, however coding time well) CONS (effect less than 0.1% numpy user I would guess): - might break existing code which in total, at least for me at this stage, speaks in favour of merging the already existing featurebranch (see #5863 <https://mail.google.com/mail/u/0/%E2%80%8Bhttps://github.com/numpy/numpy/pu…> ) or something similar into numpy master . Discussion, please! cheers, Stephan

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Re: [Numpy-discussion] making "low" optional in numpy.randint
by G Young 18 Feb '16

18 Feb '16

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> generated over the range `[0, low)`, but if `low` < 0, numbers are generated over the range `[low, highbnd)`, where `highbnd` is defined as above. 3) `low == None` and `high != None` Numbers are generated over the range `[lowbnd, high)`, where `lowbnd` is defined as above. The primary motivation was the second case, as it is more convenient to specify a 'dtype' by itself when generating such numbers in a similar vein to numpy.empty, except with initialized values. Looking forward to your feedback! Greg

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Re: [Numpy-discussion] Fwd: Multi-distribution Linux wheels - please test
by Matthew Brett 18 Feb '16

18 Feb '16

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 17 Feb '16

17 Feb '16

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". Firefox also complains that http://wheels.scipy.org is incorrectly configured with an invalid certificate. Enjoy, Chuck

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building NumPy with gcc if Python was built with icc?!?
by BERGER Christian 17 Feb '16

17 Feb '16

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 do the same and we also don't want to build every possible 3rd party module for our customers. Thanks for your help, Christian This email and any attachments are intended solely for the use of the individual or entity to whom it is addressed and may be confidential and/or privileged. If you are not one of the named recipients or have received this email in error, (i) you should not read, disclose, or copy it, (ii) please notify sender of your receipt by reply email and delete this email and all attachments, (iii) Dassault Systemes does not accept or assume any liability or responsibility for any use of or reliance on this email. For other languages, go to http://www.3ds.com/terms/email-disclaimer

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Re: [Numpy-discussion] Fwd: Windows wheels for testing
by R Schumacher 17 Feb '16

17 Feb '16

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 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> >>>

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