Mailman 3 October 2014 - NumPy-Discussion

skip samples in random number generator
by Brad Buran 03 Oct '14

03 Oct '14

Given the following: from numpy import random rs = random.RandomState(seed=1) # skip the first X billion samples x = rs.uniform(0, 10) How do I accomplish "skip the first X billion samples" (e.g. 7.2 billion)? I see that there's a numpy.random.RandomState.set_state which accepts (among other parameters) a value called "pos". This sounds promising, but the other parameters I'm not sure how to compute (e.g. the 1D array of 624 unsigned integers, etc.). I need to be able to skip ahead in the sequence to reproduce some signals that were generated for experiments. I could certainly consume and discard the first X billion samples; however, that seems to be computationally inefficient. Brad

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Problem building 64 bit numpy using MKL on Windows
by Reynolds, Jay 02 Oct '14

02 Oct '14

Hi, I've built numpy 64 bit using vc11, the Intel Fortran compiler and the MKL 'mkl_rt' library. *why? (see end of message for the reason, if interested) Any advice or assistance would be greatly appreciated. If I can offer additional information, I will happily do so. The build appears to go just fine (no errors noted), and numpy loads into python just fine as well. (I note a warning: ### Warning: python_xerbla.c is disabled ### -- however, it doesn't appear to be problematic?) I have also confirmed that numpy sees the mkl blas and lapack libs. >>> numpy.__config__.show() lapack_opt_info: libraries = ['mkl_lapack', 'mkl_rt'] library_dirs = ['C:\\Program Files (x86)\\Intel\\Composer XE 2015\\mkl\\lib\\intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['C:\\Program Files (x86)\\Intel\\Composer XE 2015\\mkl\\include'] blas_opt_info: libraries = ['mkl_rt'] library_dirs = ['C:\\Program Files (x86)\\Intel\\Composer XE 2015\\mkl\\lib\\intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['C:\\Program Files (x86)\\Intel\\Composer XE 2015\\mkl\\include'] openblas_lapack_info: NOT AVAILABLE lapack_mkl_info: libraries = ['mkl_lapack', 'mkl_rt'] library_dirs = ['C:\\Program Files (x86)\\Intel\\Composer XE 2015\\mkl\\lib\\intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['C:\\Program Files (x86)\\Intel\\Composer XE 2015\\mkl\\include'] blas_mkl_info: libraries = ['mkl_rt'] library_dirs = ['C:\\Program Files (x86)\\Intel\\Composer XE 2015\\mkl\\lib\\intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['C:\\Program Files (x86)\\Intel\\Composer XE 2015\\mkl\\include'] mkl_info: libraries = ['mkl_rt'] library_dirs = ['C:\\Program Files (x86)\\Intel\\Composer XE 2015\\mkl\\lib\\intel64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['C:\\Program Files (x86)\\Intel\\Composer XE 2015\\mkl\\include'] Everything *looks* to be in order upon casual inspection (*I think*, please correct me if I'm wrong!) However, there is no performance boost when running a few different tests in numpy (singular value decomposition, for example), and only a single thread appears to be in play. Running numpy.test('full') reveals 21 errors. For instance, LINK : fatal error LNK1104: cannot open file 'ifconsol.lib' And, the other being a recurring error with f2py, ERROR: test_size.TestSizeSumExample.test_transpose ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Program Files\Side Effects Software\Houdini 13.0.509\python27\lib\site-packages\nose\case.py", line 371, in setUp try_run(self.inst, ('setup', 'setUp')) File "C:\Program Files\Side Effects Software\Houdini 13.0.509\python27\lib\site-packages\nose\util.py", line 478, in try_run return func() File "C:\Program Files\Side Effects Software\Houdini 13.0.509\python27\lib\site-packages\numpy\f2py\tests\util.py", line 353, in setUp module_name=self.module_name) File "C:\Program Files\Side Effects Software\Houdini 13.0.509\python27\lib\site-packages\numpy\f2py\tests\util.py", line 80, in wrapper raise ret RuntimeError: Running f2py failed: ['-m', '_test_ext_module_5403', 'c:\\users\\jareyn~1\\appdata\\local\\temp\\tmpvykewl \\foo.f90'] Reading .f2py_f2cmap ... Mapping "real(kind=rk)" to "double" Succesfully applied user defined changes from .f2py_f2cmap Everything that requires configuration appears to be in agreement with this Intel Application Note, minus use of the Intel C++ compiler: https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl I have also referenced the Windows build docs on scipy.org: http://www.scipy.org/scipylib/building/windows.html#building-scipy Some info about my configuration: site.cfg: include_dirs = C:\Program Files (x86)\Intel\Composer XE 2015\mkl\include library_dirs = C:\Program Files (x86)\Intel\Composer XE 2015\mkl\lib\intel64 mkl_libs = mkl_rt PATH = (paths separated by line for easy reading) C:\Program Files\Side Effects Software\Houdini 13.0.509\python27; C:\Program Files\Side Effects Software\Houdini 13.0.509\python27\Scripts; C:\Program Files (x86)\Microsoft Visual Studio 11.0\VC\bin\x86_amd64; C:\Program Files (x86)\Intel\Composer XE 2015\bin\intel64 LD_LIBRARY_PATH = C:\Program Files (x86)\Microsoft Visual Studio 11.0\VC\bin\x86_amd64; C:\Program Files (x86)\Intel\Composer XE 2015\bin\intel64; C:\Program Files (x86)\Intel\Composer XE 2015\mkl\lib\intel64; C:\Program Files (x86)\Intel\Composer XE 2015\compiler\lib\intel64 Thank you in advance for your time, -Jay ===== *why am I doing this? The reason I'm doing this is because I need numpy with MKL to run with the version of python that comes packaged with Houdini (Python 2.7.5 (default, Oct 24 2013, 17:49:49) [MSC v.1700 64 bit (AMD64)] on win32). So, downloading a prebuilt 64 bit numpy isn't an option due to the unavailability of a compatible compiler version.

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Re: [Numpy-discussion] Interpolation using `np.interp()` with periodic x-coordinates (Saullo Castro)
by Saullo Castro 02 Oct '14

02 Oct '14

Jaime has helped me and PR#5117 is getting ready to be merged <https://github.com/numpy/numpy/pull/5117>... 2014-10-02 19:00 GMT+02:00 <numpy-discussion-request(a)scipy.org>: > Send NumPy-Discussion mailing list submissions to > numpy-discussion(a)scipy.org > > To subscribe or unsubscribe via the World Wide Web, visit > http://mail.scipy.org/mailman/listinfo/numpy-discussion > or, via email, send a message with subject or body 'help' to > numpy-discussion-request(a)scipy.org > > You can reach the person managing the list at > numpy-discussion-owner(a)scipy.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of NumPy-Discussion digest..." > > > Today's Topics: > > 1. Re: Interpolation using `np.interp()` with periodic > x-coordinates (Charles R Harris) > 2. Re: Proposal: add ndarray.keys() to return dtype.names > (Sebastian Wagner) > 3. Re: Proposal: add ndarray.keys() to return dtype.names > (John Zwinck) > 4. skip samples in random number generator (Brad Buran) > 5. Re: skip samples in random number generator (Robert Kern) > 6. Re: skip samples in random number generator (Nathaniel Smith) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 1 Oct 2014 18:12:56 -0600 > From: Charles R Harris <charlesr.harris(a)gmail.com> > Subject: Re: [Numpy-discussion] Interpolation using `np.interp()` with > periodic x-coordinates > To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> > Message-ID: > <CAB6mnxJwuw8k=NCkkUyKioCvLUy3XfUQkR= > o-DgkxWQfvWOB6A(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > On Wed, Sep 24, 2014 at 3:57 PM, Saullo Castro <saullogiovani(a)gmail.com> > wrote: > > > From the closed pull request PR #5109: > > > > https://github.com/numpy/numpy/pull/5109 > > > > it came out that the a good implementation would be adding a parameter > > `period`. I would like to know about the community's interest for this > > implementation. > > > > The modification are shown here: > > > > > https://github.com/saullocastro/numpy/compare/interp_with_period?expand=1 > > > > Please, let me know about your feedback. > > > > I don't have any problem with allowing periodic interpolation. > > Chuck >

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Proposal: add ndarray.keys() to return dtype.names
by John Zwinck 02 Oct '14

02 Oct '14

I first proposed this on GitHub: https://github.com/numpy/numpy/issues/5134 ; jaimefrio requested that I bring it to this list for discussion. My proposal is to add a keys() method to NumPy's array class ndarray. The behavior would be to return self.dtype.names, i.e. the "column names" for a structured array (and None when dtype.names is None, which it is for pure numeric arrays without named columns). I originally proposed to add a values() method also, but I am tabling that for now so we needn't discuss it in this thread. The motivation is to enhance the ability to use duck typing with NumPy arrays, Python dicts, and other types like Pandas DataFrames, h5py Files, and more. It's a fairly common thing to want to get the "keys" of a container, where "keys" is understood to be a sequence of values one can pass to __getitem__(), and this is exactly what I'm aiming at. Thoughts? John Zwinck

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Interpolation using `np.interp()` with periodic x-coordinates
by Saullo Castro 01 Oct '14

01 Oct '14

>From the closed pull request PR #5109: https://github.com/numpy/numpy/pull/5109 it came out that the a good implementation would be adding a parameter `period`. I would like to know about the community's interest for this implementation. The modification are shown here: https://github.com/saullocastro/numpy/compare/interp_with_period?expand=1 Please, let me know about your feedback. Regards, Saullo

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