Mailman 3 March 2013 - NumPy-Discussion

(@Pat Marion) Re: Yes, this one again "ImportError: No module named multiarray"
by Aron Ahmadia March 13, 2013

March 13, 2013

Pat Marion at Kitware did some work on this, I'm pinging him on the thread. A On Tue, Mar 12, 2013 at 2:05 PM, Dinesh B Vadhia <dineshbvadhia(a)hotmail.com>wrote: > ** > Does that mean numpy won't work with freeze/create_executable type of > tools or is there a workaround? > > > *From:* Aron Ahmadia <aron(a)ahmadia.net> > *Sent:* Tuesday, March 12, 2013 6:17 AM > *To:* Discussion of Numerical Python <numpy-discussion(a)scipy.org> > *Subject:* Re: [Numpy-discussion] Yes,this one again "ImportError: No > module named multiarray" > > multiarray is an extension module that lives within numpy/core, that is, > when, "import multiarray" is called, (and it's the first imported extension > module in numpy), multiarray.ext (ext being dll on Windows I guess), gets > dynamically loaded. > > "No module named multiarray" is indicating problems with your freeze > setup. Most of these tools don't support locally imported extension > modules. > > Does this help you get oriented on your problem? > > A > > > On Tue, Mar 12, 2013 at 1:01 PM, Dinesh B Vadhia < > dineshbvadhia(a)hotmail.com> wrote: > >> ** >> I've been using Numpy/Scipy for >5 years so know a little on how to get >> around them. Recently, I've needed to either freeze or create executables >> with tools such as PyInstaller, Cython, Py2exe and others on both Windows >> (XP 32-bit, 7 64-bit) and Ubuntu (12.04) Linux (64-bit). The test >> program (which runs perfectly with the Python interpreter) is very simple: >> >> import numpy >> >> def main(): >> print numpy.array([12, 23, 34, 45, 56, 67, 78, 89, 90]) >> return >> >> if __name__ == '__main__': >> main() >> >> The software versions are Python 2.7.3, Numpy 1.7.0, and Scipy 0.11. The >> "import numpy" causes an "ImportError: No module named multiarray". After >> endless Googling, I am none the wiser about what (really) causes the >> ImportError let alone what the solution is. The Traceback, similar to >> others found on the web, is: >> >> Traceback (most recent call last): >> File "test.py", ... >> File "C:\Python27\lib\site-packages\numpy\__init__.py", line 154, in >> <module> >> import add_newdocs >> File "C:\Python27\lib\site-packages\numpy\add_newdocs.py", line 9, in >> <module> >> from numpy.lib import add_newdoc >> File "C:\Python27\lib\site-packages\numpy\lib\__init__.py", line 4, in >> <module> >> from type_check import * >> File "C:\Python27\lib\site-packages\numpy\lib\type_check.py", line 8, >> in <module> >> import numpy.core.numeric as _nx >> File "C:\Python27\lib\site-packages\numpy\core\__init__.py", line 5, in >> <module> >> import multiarray >> ImportError: No module named multiarray. >> >> Could someone shed some light on this - please? Thx. >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion(a)scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >

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Performance of einsum?
by Jaakko Luttinen March 13, 2013

March 13, 2013

Hi, I was wondering if someone could provide some intuition on the performance of einsum? I have found that sometimes it is extremely efficient but sometimes it is several orders of magnitudes slower compared to some other approaches, for instance, using multiple dot-calls. My intuition is that the computation time of einsum is linear with respect to the size of the "index space", that is, the product of the maximums of the indices. So, for instance computing the dot product of three matrices A*B*C would not be efficient as einsum('ij,jk,kl->il', A, B, C) because there are four indices i=1,...,I, j=1,...,J, k=1,...,K and l=1,...,L so the total computation time is O(I*J*K*L) which is much worse than with two dot products O(I*J*K+J*K*L), or with two einsum-calls for Y=A*B and Y*C. On the other hand, computing einsum('ij,ij,ij->i', A, B, C) would be "efficient" because the computation time is only O(I*J). Is this intuition roughly correct or how could I intuitively understand when the usage of einsum is bad? Best regards, Jaakko

1 0

Bug in einsum?
by Jaakko Luttinen March 13, 2013

March 13, 2013

Hi, I have encountered a very weird behaviour with einsum. I try to compute something like R*A*R', where * denotes a kind of "matrix multiplication". However, for particular shapes of R and A, the results are extremely bad. I compare two einsum results: First, I compute in two einsum calls as (R*A)*R'. Second, I compute the whole result in one einsum call. However, the results are significantly different for some shapes. My test: import numpy as np for D in range(30): A = np.random.randn(100,D,D) R = np.random.randn(D,D) Y1 = np.einsum('...ik,...kj->...ij', R, A) Y1 = np.einsum('...ik,...kj->...ij', Y1, R.T) Y2 = np.einsum('...ik,...kl,...lj->...ij', R, A, R.T) print("D=%d" % D, np.allclose(Y1,Y2), np.linalg.norm(Y1-Y2)) Output: D=0 True 0.0 D=1 True 0.0 D=2 True 8.40339658678e-15 D=3 True 8.09995399928e-15 D=4 True 3.59428803435e-14 D=5 False 34.755610184 D=6 False 28.3576558351 D=7 False 41.5402690906 D=8 True 2.31709582841e-13 D=9 False 36.0161112799 D=10 True 4.76237746912e-13 D=11 True 4.57944440782e-13 D=12 True 4.90302218301e-13 D=13 True 6.96175851271e-13 D=14 True 1.10067181384e-12 D=15 True 1.29095933163e-12 D=16 True 1.3466837332e-12 D=17 True 1.52265065763e-12 D=18 True 2.05407923852e-12 D=19 True 2.33327630748e-12 D=20 True 2.96849358082e-12 D=21 True 3.31063706175e-12 D=22 True 4.28163620455e-12 D=23 True 3.58951880681e-12 D=24 True 4.69973694769e-12 D=25 True 5.47385264567e-12 D=26 True 5.49643316347e-12 D=27 True 6.75132988402e-12 D=28 True 7.86435437892e-12 D=29 True 7.85453681029e-12 So, for D={5,6,7,9}, allclose returns False and the error norm is HUGE. It doesn't seem like just some small numerical inaccuracy because the error norm is so large. I don't know which one is correct (Y1 or Y2) but at least either one is wrong in my opinion. I ran the same test several times, and each time same values of D fail. If I change the shapes somehow, the failing values of D might change too, but I usually have several failing values. I'm running the latest version from github (commit bd7104cef4) under Python 3.2.3. With NumPy 1.6.1 under Python 2.7.3 the test crashes and Python exits printing "Floating point exception". This seems so weird to me that I wonder if I'm just doing something stupid.. Thanks a lot for any help! Jaakko

1 0

Vectorize and ufunc attribute
by T J March 13, 2013

March 13, 2013

Prior to 1.7, I had working compatibility code such as the following: if has_good_functions: # http://projects.scipy.org/numpy/ticket/1096 from numpy import logaddexp, logaddexp2 else: logaddexp = vectorize(_logaddexp, otypes=[numpy.float64]) logaddexp2 = vectorize(_logaddexp2, otypes=[numpy.float64]) # Run these at least once so that .ufunc.reduce exists logaddexp([1.,2.,3.],[1.,2.,3.]) logaddexp2([1.,2.,3.],[1.,2.,3.]) # And then make reduce available at the top level logaddexp.reduce = logaddexp.ufunc.reduce logaddexp2.reduce = logaddexp2.ufunc.reduce The point was that I wanted to treat the output of vectorize as a hacky drop-in replacement for a ufunc. In 1.7, I discovered that vectorize had changed (https://github.com/numpy/numpy/pull/290), and now there is no longer a ufunc attribute at all. Should this be added back in? Besides hackish drop-in replacements, I see value in to being able to call reduce, accumulate, etc (when possible) on the output of vectorize().

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Request code review of numpy.i changes
by Bill Spotz March 12, 2013

March 12, 2013

https://github.com/wfspotz/numpy/compare/numpy-swig ** Bill Spotz ** ** Sandia National Laboratories Voice: (505)845-0170 ** ** P.O. Box 5800 Fax: (505)284-0154 ** ** Albuquerque, NM 87185-0370 Email: wfspotz(a)sandia.gov **

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unclear output format for numpy.argmax()
by soumen ganguly March 12, 2013

March 12, 2013

Hello, There are some doubts that i have regarding the argmax() method of numpy.As described in reference doc's of numpy,argmax(axis=None,out=None) returns the indices of the maximum value along the given axis(In this case 0 is default). So, i tried to implement the method to a 2d array with elements say,[[1,2,3],[4,5,6]] along the axis 1.The output to this code is [2,2] and when i implement it along the axis 0,it outputs [1,1,1].I dont see the connection to this output with the scope of argmax method. I would appreciate a detailed insight to the argmax method.

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Casting and promotion rules (e.g. int + uint64 => float)
by Sergio Callegari March 11, 2013

March 11, 2013

Hi, I have noticed that numpy introduces some unexpected type casts, that are in some cases problematic. A very weird cast is int + uint64 -> float for instance, consider the following snippet: import numpy as np a=np.uint64(1) a+1 -> 2.0 this cast is quite different from what other programming languages (e.g., C) would do in this case, so it already comes unexpected. Furthermore, an int64 (or an uint64) is too large to fit into a float, hence this automatic conversion also results in data loss! For instance consider: a=np.uint64(18446744073709551614) a+np.uint64(1) -> 18446744073709551615 # CORRECT! a+1 -> 1.8446744073709552e+19 # Actually 1.84467440737095516160e+19 - LOSS OF DATA in fact np.uint64(a+1) -> 0 Weird, isn't it? Another issue is that variables unexpectedly change type with accumulation operators a=np.uint64(1) a+=1 now a is float I believe that some casting/promotion rules should be revised, since they now lead to difficult to catch, intermittent errors. In case this cannot be done immediately, I suggest at least documenting these promotions, providing examples on how to code many conventional tasks. E.g., incrementing an integer of unknown size b=a+type(a)(1) I have also reported this in https://github.com/numpy/numpy/issues/3118 Thanks!

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Numpy 1.7.0 with Intel MKL 11.0.2.146
by QT March 11, 2013

March 11, 2013

Dear all, I'm at my wits end. I've followed Intel's own instructions<http://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl>on how to compile Numpy with Intel MKL. Everything compiled and linked fine and I've installed it locally in my user folder...There is one nasty problem. When one calls the numpy library to do some computation, it does not use all of the available threads. I have 8 "cores" on my machine and it only uses 4 of them. The MKL_NUM_THREADS environmental variable can be set to tune the number of threads but setting it to 8 does not change anything. Indeed, setting it to 3 does limit the threads to 3....What is going on? As a comparison, the numpy (version 1.4.1, installed from yum, which uses BLAS+ATLAS) uses all 8 threads. I do not get this. You can run this test program python -mtimeit -s'import numpy as np; a = np.random.randn(1e3,1e3)' 'np.dot(a, a)' There is one saving grace, the local numpy built with MKL is much faster than the system's numpy. I hope someone can help me. Searching the internet has been fruitless. Best, Quyen My site.cfg for numpy (1.7.0) [mkl] library_dirs = /opt/intel/mkl/lib/intel64 include_dirs = /opt/intel/mkl/include mkl_libs = mkl_rt lapack_libs = I've edited line 37 of numpy/distutils/intelcompiler.py self.cc_exe = 'icc -O3 -fPIC -fp-model strict -fomit-frame-pointer -openmp -parallel -DMKL_ILP64' Also line 54 of numpy/distutils/fcompiler/intel.py return ['-i8 -xhost -openmp -fp-model strict'] My .bash_profile also contains the lines: source /opt/intel/bin/compilervars.sh intel64 source /opt/intel/mkl/bin/mklvars.sh intel64 The above is needed to set the LD_LIBRARY_PATH so that Python can source the intel dynamic library when numpy is called.

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Compile time flag for numpy
by Sebastian Berg March 9, 2013

March 9, 2013

Hey, how would I go about making a compile time flag for numpy to use as a macro? The reason is: https://github.com/numpy/numpy/pull/2735 so that it would be possible to compile numpy differently for debugging if software depending on numpy is broken by this change. Regards, Sebastian

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aligned / unaligned structured dtype behavior (was: GSOC 2013)
by Kurt Smith March 8, 2013

March 8, 2013

On Wed, Mar 6, 2013 at 12:12 PM, Kurt Smith <kwmsmith(a)gmail.com> wrote: > On Wed, Mar 6, 2013 at 4:29 AM, Francesc Alted <francesc(a)continuum.io> wrote: >> >> I would not run too much. The example above takes 9 bytes to host the >> structure, while a `aligned=True` will take 16 bytes. I'd rather let >> the default as it is, and in case performance is critical, you can >> always copy the unaligned field to a new (homogeneous) array. > > Yes, I can absolutely see the case you're making here, and I made my > "vote" with the understanding that `aligned=False` will almost > certainly stay the default. Adding 'aligned=True' is simple for me to > do, so no harm done. > > My case is based on what's the least surprising behavior: C structs / > all C compilers, the builtin `struct` module, and ctypes `Structure` > subclasses all use padding to ensure aligned fields by default. You > can turn this off to get packed structures, but the default behavior > in these other places is alignment, which is why I was surprised when > I first saw that NumPy structured dtypes are packed by default. > Some surprises with aligned / unaligned arrays: #----------------------------- import numpy as np packed_dt = np.dtype((('a', 'u1'), ('b', 'u8')), align=False) aligned_dt = np.dtype((('a', 'u1'), ('b', 'u8')), align=True) packed_arr = np.ones((10**6,), dtype=packed_dt) aligned_arr = np.ones((10**6,), dtype=aligned_dt) print "all(packed_arr['a'] == aligned_arr['a'])", np.all(packed_arr['a'] == aligned_arr['a']) # True print "all(packed_arr['b'] == aligned_arr['b'])", np.all(packed_arr['b'] == aligned_arr['b']) # True print "all(packed_arr == aligned_arr)", np.all(packed_arr == aligned_arr) # False (!!) #----------------------------- I can understand what's likely going on under the covers that makes these arrays not compare equal, but I'd expect that if all columns of two structured arrays are everywhere equal, then the arrays themselves would be everywhere equal. Bug? And regarding performance, doing simple timings shows a 30%-ish slowdown for unaligned operations: In [36]: %timeit packed_arr['b']**2 100 loops, best of 3: 2.48 ms per loop In [37]: %timeit aligned_arr['b']**2 1000 loops, best of 3: 1.9 ms per loop Whereas summing shows just a 10%-ish slowdown: In [38]: %timeit packed_arr['b'].sum() 1000 loops, best of 3: 1.29 ms per loop In [39]: %timeit aligned_arr['b'].sum() 1000 loops, best of 3: 1.14 ms per loop

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SciPy John Hunter Excellence in Plotting Contest
by Michael Droettboom March 8, 2013

March 8, 2013

Apologies for any accidental cross-posting. Email not displaying correctly? View it in your browser. <http://us1.campaign-archive1.com/?u=e91b4574d5d1709a9dc4f7ab7&id=999d7ba343…> Scientific Computing with Python-Austin, Texas-June 24-29, 2013 SciPy John Hunter Excellence in Plotting Contest In memory of John Hunter, we are pleased to announce the first SciPy John Hunter Excellence in Plotting Competition. This open competition aims to highlight the importance of quality plotting to scientific progress and showcase the capabilities of the current generation of plotting software. Participants are invited to submit scientific plots to be judged by a panel. The winning entries will be announced and displayed at the conference. NumFOCUS is graciously sponsoring cash prizes for the winners in the following amounts: * 1st prize: $500 * 2nd prize: $200 * 3rd prize: $100 Instructions * Entries must be submitted by April 3 via e-mail <mailto:plotting-contest@scipy.org>. * Plots may be produced with any combination of Python-based tools (it is not required that they use matplotlib, for example). * Source code for the plot must be provided, along with a rendering of the plot in a vector format (PDF, PS, etc.). If the data can not be shared for reasons of size or licensing, "fake" data may be substituted, along with an image of the plot using real data. * Entries will be judged on their clarity, innovation and aesthetics, but most importantly for their effectiveness in illuminating real scientific work. Entrants are encouraged to submit plots that were used during the course of research, rather than merely being hypothetical. * SciPy reserves the right to display the entry at the conference, use in any materials or on its website, providing attribution to the original author(s). Important dates: * April 3rd: Plotting submissions due * Monday-Tuesday, June 24 - 25: SciPy 2013 Tutorials, Austin TX * Wednesday-Thursday, June 26 - 27: SciPy 2013 Conference, Austin TX * Winners will be announced during the conference days * Friday-Saturday, June 27 - 28: SciPy 2013 Sprints, Austin TX & remote We look forward to exciting submissions that push the boundaries of plotting, in this, our first attempt at this kind of competition. The SciPy Plotting Contest Organizer -Michael Droettboom, Space Telescope Science Institute You are receiving this email because you subscribed to the mailing list or registered for the SciPy 2010 or SciPy 2011 conference in Austin, TX. Unsubscribe <http://scipy.us1.list-manage.com/unsubscribe?u=e91b4574d5d1709a9dc4f7ab7&id…> mdboom(a)gmail.com <mailto:mdboom@gmail.com> from this list | Forward to a friend <http://us1.forward-to-friend1.com/forward?u=e91b4574d5d1709a9dc4f7ab7&id=99…> | Update your profile <http://scipy.us1.list-manage.com/profile?u=e91b4574d5d1709a9dc4f7ab7&id=069…> *Our mailing address is:* Enthought, Inc. 515 Congress Ave. Austin, TX 78701 Add us to your address book <http://scipy.us1.list-manage.com/vcard?u=e91b4574d5d1709a9dc4f7ab7&id=069dc…> /Copyright (C) 2013 Enthought, Inc. All rights reserved./ -- Michael Droettboom http://www.droettboom.com/

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Definition of dot function
by Mayank Daga March 7, 2013

March 7, 2013

Hi, Can someone point me to the definition of dot() in the numpy source? The only instance of 'def dot()' I found was in numpy/ma/extras.py but that does not seem to be the correct one. ~mayank -- Mayank Daga "Nothing Succeeds Like Success"

3 2

feature tracking in numpy/scipy
by Scott Collis March 7, 2013

March 7, 2013

Good afternoon list, I am looking at feature tracking in a 2D numpy array, along the lines of Dixon and Wiener 1993 (for tracking precipitating storms) Identifying features based on threshold is quite trivial using ndimage.label b_fld=np.zeros(mygrid.fields['rain_rate_A']['data'].shape) rr=10 b_fld[mygrid.fields['rain_rate_A']['data'] > rr]=1.0 labels, numobjects = ndimage.label(b_fld[0,0,:,:]) (note mygrid.fields['rain_rate_A']['data'] is dimensions time,height, y, x) using the matplotlib contouring and fetching the vertices I can get a nice list of polygons of rain rate above a certain threshold… Now from here I can just go and implement the Dixon and Wiener methodology but I thought I would check here first to see if anyone know of a object/feature tracking algorithm in numpy/scipy or using numpy arrays (it just seems like something people would want to do!).. i.e. something that looks back and forward in time and identifies polygon movement and identifies objects with temporal persistence.. Cheers! Scott Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology. Journal of Atmospheric and Oceanic Technology, 10, 785–797, doi:10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2. http://journals.ametsoc.org/doi/abs/10.1175/1520-0426%281993%29010%3C0785%3…

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polyfit with fixed points
by Jaime Fernández del Río March 7, 2013

March 7, 2013

A couple of days back, answering a question in StackExchange ( http://stackoverflow.com/a/15196628/110026), I found myself using Lagrange multipliers to fit a polynomial with least squares to data, making sure it went through some fixed points. This time it was relatively easy, because some 5 years ago I came across the same problem in real life, and spent the better part of a week banging my head against it. Even knowing what you are doing, it is far from simple, and in my own experience very useful: I think the only time ever I have fitted a polynomial to data with a definite purpose, it required that some points were fixed. Seeing that polyfit is entirely coded in python, it would be relatively straightforward to add support for fixed points. It is also something I feel capable, and willing, of doing. * Is such an additional feature something worthy of investigating, or will it never find its way into numpy.polyfit? * Any ideas on the best syntax for the extra parameters? Thanks, Jaime -- (\__/) ( O.o) ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.

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scipy.optimize.fminbound bound violation
by Alan G Isaac March 7, 2013

March 7, 2013

Under what conditions should I expect fminbound to call the supplied function with argument values substantially outside the user-provided bounds? Thanks, Alan Isaac

1 0

Numpy deprecation schedule
by Nathaniel Smith March 6, 2013

March 6, 2013

A number of items on the 1.8 todo list are reminders to remove things that we deprecated in 1.7, and said we would remove in 1.8, e.g.: https://github.com/numpy/numpy/issues/596 https://github.com/numpy/numpy/issues/294 But, since 1.8 is so soon after 1.7, we probably shouldn't actually do that. I suggest we switch to a time-based deprecation schedule, where instead of saying "this will be removed in N releases" we say "this will be removed in the first release on or after (now+N months)". I also suggest that we set N=12, because it's a round number, it roughly matches numpy's historical release cycle, and because AFAICT that's the number that python itself uses for core and stdlib deprecations. Thoughts? -n

4 9

scipy.weave + nose testing no longer works with numpy 1.7.0
by Marcel Stimberg March 6, 2013

March 6, 2013

Hi, I noticed that our unit tests running under nose and using scipy.weave started to fail with numpy 1.7.0 because of a change in numpy.distutils.exec_command (called by scipy.weave) which now assumes that sys.stdout always provides a fileno function (which fails because nose redirect stdout to a cStringIO). I guess the combination of scipy.weave and nose is not that unusual for scientific software, maybe 1.7.1 could make the exec_command a bit more robust in that regard? I filed the issue as #2999 in github (including a simple example triggering the error): https://github.com/numpy/numpy/issues/2999 Thanks Marcel

2 1

GSOC 2013
by Todd March 6, 2013

March 6, 2013

Is numpy planning to participate in GSOC this year, either on their own or as a part of another group? If so, should we start trying to get some project suggestions together?

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a question about freeze on numpy 1.7.0
by Gelin Yan March 6, 2013

March 6, 2013

Hi All When I used numpy 1.7.0 with cx_freeze 4.3.1 on windows, I quickly found out even a simple "import numpy" may lead to program failed with following exception: "AttributeError: 'module' object has no attribute 'sys' After a poking around some codes I noticed /numpy/core/__init__.py has a line 'del sys' at the bottom. After I commented this line, and repacked the whole program, It ran fine. I also noticed this 'del sys' didn't exist on numpy 1.6.2 I am curious why this 'del sys' should be here and whether it is safe to omit it. Thanks. Regards gelin yan

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Help trying to fix issue 368 on Github (math functions fail confusingly on long integers (and object arrays generally))
by Dan Blanchard March 6, 2013

March 6, 2013

Hi, I've been trying to take a crack at fixing https://github.com/numpy/numpy/issues/368, and I think I've identified all of the affected functions and even a potential fix, but I'm new to the Python C API and the numpy source, so if anyone has time to look at the discussion on Github and chime in with suggestions, I'd be glad to help finish getting this patched up. It is currently very frustrating that many of the math functions do not work with longs. Thanks, Dan

1 0

embedding numpy ImportError: numpy.core.multiarray failed to import
by Sunghwan Choi March 6, 2013

March 6, 2013

Hi, I tried to embedding numpy in C++ but I got ImportError: numpy.core.multiarray failed to import Do you know any ways to solve this problem? I copy my codes and error message Makefile CXX= icpc all: exe clean: rm -rf *.o exe exe: test.o $(CXX) -o exe test.o -L/home/shchoi/program/epd/lib/ -lpython2.7 test.o : test.cpp $(CXX) -c test.cpp -I/home/shchoi/program/epd/lib/python2.7/site-packages/numpy/core/include/nu mpy/ -I/home/shchoi/program/epd/include/python2.7 tmp.cpp #include "Python.h" #include "arrayobject.h" #include <stdio.h> #include <iostream> extern "C" void Py_Initialize(); extern "C" void PyErr_Print(); using namespace std; int main(int argc, char* argv[]) { double answer = 0; PyObject *modname, *mod, *mdict, *func, *stringarg, *args, *rslt; Py_Initialize(); import_array(); modname = PyString_FromString("numpy"); mod = PyImport_Import(modname); PyErr_Print(); cout << mod << endl; Py_Finalize(); return 0; } $ make icpc -c test.cpp -I/home/shchoi/program/epd/lib/python2.7/site-packages/numpy/core/include/nu mpy/ -I/home/shchoi/program/epd/include/python2.7 test.cpp(15): warning #117: non-void function "main" should return a value import_array(); ^ icpc -o exe test.o -L/home/shchoi/program/epd/lib/ -lpython2.7 #-L/home/shchoi/program/epd/lib/python2.7/site-packages/numpy/core/ $ ./exe ImportError: numpy.core.multiarray failed to import Sunghwan Choi

1 0

scipy_distutils.fcompiler
by Gergely Buday March 5, 2013

March 5, 2013

Hi there, I try to compile a program developed with scipy. It is installed on my Ubuntu 12.04 box but upon make I get: Traceback (most recent call last): File "/usr/local/bin/f2py", line 4, in <module> f2py2e.main() File "/usr/local/lib/python2.7/dist-packages/f2py2e/f2py2e.py", line 677, in main run_compile() File "/usr/local/lib/python2.7/dist-packages/f2py2e/f2py2e.py", line 536, in run_compile import scipy_distutils.fcompiler ImportError: No module named scipy_distutils.fcompiler What should I do to fix this? I have scipy Version: 0.9.0+dfsg1-1ubuntu2 - Gergely

3 2

Remove interactive setup
by Charles R Harris March 5, 2013

March 5, 2013

In distutils there are three files that provide some interactive setup: 1. numpy/distutils/core.py 2. numpy/distutils/fcompiler/gnu.py 3. numpy/distutils/interactive.py In Python3 `raw_input` has been renamed 'input' and python2 'input' is gone. I propose that the easiest solution to this compatibility problem is to remove all support for interactive numpy setup. Thoughts? Chuck

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Adding .abs() method to the array object
by Till Stensitzki March 4, 2013

March 4, 2013

Hello, i know that the array object is already crowded, but i would like to see the abs method added, especially doing work on the console. Considering that many much less used functions are also implemented as a method, i don't think adding one more would be problematic. greetings Till

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step paramter for linspace
by Sebastian Berg March 4, 2013

March 4, 2013

Hi, there has been a request on the issue tracker for a step parameter to linspace. This is of course tricky with the imprecision of floating point numbers. As a trade off, I was thinking of a step parameter that is used to calculate the integer number of steps. However to be certain that it never misbehaves, doing this strict up to the numerical precision of the (float) numbers. Effectively this means: In [9]: np.linspace(0, 1.2, step=0.3) Out[9]: array([ 0. , 0.3, 0.6, 0.9, 1.2]) In [10]: np.linspace(0, 1.2+5-5, step=0.3) Out[10]: array([ 0. , 0.3, 0.6, 0.9, 1.2]) In [11]: np.linspace(0, 1.2+500-500, step=0.3) ValueError: could not determine exact number of samples for given step I.e. the last fails, because 1.2 + 500 - 500 == 1.1999999999999886, which is an error that is larger then the imprecision of floating point numbers. Is this considered useful, or as it can easily fail for calculated numbers, and is thus only a convenience, it is not? Regards, Sebastian

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