Mailman 3 June 2012 - NumPy-Discussion

Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 69, Issue 77
by James A. Bednar 26 Jun '12

26 Jun '12

| From: Rebekah Pratt | Date: Jun 25 23:59:59 2012 -0400 | | Hey, greetings from orlando! How has re entry to texas been? Our | holiday is going well, although a little fast. The whole disney | experience is cooler than I was expecting. We are all a bit tired | out though, and adah moody as anything. Anyway best get to sleep, | but wanted to say hi, and hope all going well with you. Sounds exciting! We've been limping along with very little sleep, but it's good to be home. Lots of sunshine and swimming, though 107 degrees F is not really all that fun. The kids are loving it here. Not much time to check email, so it's really piling up. Hope you have a good trip back... Jim -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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Matrix rank default tolerance - is it too low?
by JB Poline 26 Jun '12

26 Jun '12

>On Sat, Jun 16, 2012 at 4:39 PM, Nathaniel Smith <njs(a)pobox.com> wrote: >> On Sat, Jun 16, 2012 at 9:03 PM, Matthew Brett <matthew.brett(a)gmail.com> wrote: >>> Hi, >>> >>> On Sat, Jun 16, 2012 at 10:40 AM, Nathaniel Smith <njs(a)pobox.com> wrote: >>>> On Fri, Jun 15, 2012 at 4:10 AM, Charles R Harris >>>> <charlesr.harris(a)gmail.com> wrote: >>>>> >>>>> >>>>> On Thu, Jun 14, 2012 at 8:06 PM, Matthew Brett <matthew.brett(a)gmail.com> >>>>> wrote: >>>>>> >>>>>> Hi, >>>>>> >>>>>> I noticed that numpy.linalg.matrix_rank sometimes gives full rank for >>>>>> matrices that are numerically rank deficient: >>>>>> >>>>>> If I repeatedly make random matrices, then set the first column to be >>>>>> equal to the sum of the second and third columns: >>>>>> >>>>>> def make_deficient(): >>>>>> X = np.random.normal(size=(40, 10)) >>>>>> deficient_X = X.copy() >>>>>> deficient_X[:, 0] = deficient_X[:, 1] + deficient_X[:, 2] >>>>>> return deficient_X >>>>>> >>>>>> then the current numpy.linalg.matrix_rank algorithm returns full rank >>>>>> (10) in about 8 percent of cases (see appended script). >>>>>> >>>>>> I think this is a tolerance problem. The ``matrix_rank`` algorithm >>>>>> does this by default: >>>>>> >>>>>> S = spl.svd(M, compute_uv=False) >>>>>> tol = S.max() * np.finfo(S.dtype).eps >>>>>> return np.sum(S > tol) >>>>>> >>>>>> I guess we'd we want the lowest tolerance that nearly always or always >>>>>> identifies numerically rank deficient matrices. I suppose one way of >>>>>> looking at whether the tolerance is in the right range is to compare >>>>>> the calculated tolerance (``tol``) to the minimum singular value >>>>>> (``S.min()``) because S.min() in our case should be very small and >>>>>> indicate the rank deficiency. The mean value of tol / S.min() for the >>>>>> current algorithm, across many iterations, is about 2.8. We might >>>>>> hope this value would be higher than 1, but not much higher, otherwise >>>>>> we might be rejecting too many columns. >>>>>> >>>>>> Our current algorithm for tolerance is the same as the 2-norm of M * >>>>>> eps. We're citing Golub and Van Loan for this, but now I look at our >>>>>> copy (p 261, last para) - they seem to be suggesting using u * |M| >>>>>> where u = (p 61, section 2.4.2) eps / 2. (see [1]). I think the Golub >>>>>> and Van Loan suggestion corresponds to: >>>>>> >>>>>> tol = np.linalg.norm(M, np.inf) * np.finfo(M.dtype).eps / 2 >>>>>> >>>>>> This tolerance gives full rank for these rank-deficient matrices in >>>>>> about 39 percent of cases (tol / S.min() ratio of 1.7) >>>>>> >>>>>> We see on p 56 (section 2.3.2) that: >>>>>> >>>>>> m, n = M.shape >>>>>> 1 / sqrt(n) . |M|_{inf} <= |M|_2 >>>>>> >>>>>> So we can get an upper bound on |M|_{inf} with |M|_2 * sqrt(n). Setting: >>>>>> >>>>>> tol = S.max() * np.finfo(M.dtype).eps / 2 * np.sqrt(n) >>>>>> >>>>>> gives about 0.5 percent error (tol / S.min() of 4.4) >>>>>> >>>>>> Using the Mathworks threshold [2]: >>>>>> >>>>>> tol = S.max() * np.finfo(M.dtype).eps * max((m, n)) >>>>>> >>>>>> There are no false negatives (0 percent rank 10), but tol / S.min() is >>>>>> around 110 - so conservative, in this case. >>>>>> >>>>>> So - summary - I'm worrying our current threshold is too small, >>>>>> letting through many rank-deficient matrices without detection. I may >>>>>> have misread Golub and Van Loan, but maybe we aren't doing what they >>>>>> suggest. Maybe what we could use is either the MATLAB threshold or >>>>>> something like: >>>>>> >>>>>> tol = S.max() * np.finfo(M.dtype).eps * np.sqrt(n) >>>>>> >>>>>> - so 2 * the upper bound for the inf norm = 2 * |M|_2 * sqrt(n) . This >>>>>> gives 0 percent misses and tol / S.min() of 8.7. >>>>>> >>>>>> What do y'all think? >>>>>> >>>>>> Best, >>>>>> >>>>>> Matthew >>>>>> >>>>>> [1] >>>>>> http://matthew-brett.github.com/pydagogue/floating_error.html#machine-epsil… >>>>>> [2] http://www.mathworks.com/help/techdoc/ref/rank.html >>>>>> >>>>>> Output from script: >>>>>> >>>>>> Percent undetected current: 9.8, tol / S.min(): 2.762 >>>>>> Percent undetected inf norm: 39.1, tol / S.min(): 1.667 >>>>>> Percent undetected upper bound inf norm: 0.5, tol / S.min(): 4.367 >>>>>> Percent undetected upper bound inf norm * 2: 0.0, tol / S.min(): 8.734 >>>>>> Percent undetected MATLAB: 0.0, tol / S.min(): 110.477 >>>>>> >>>>>> <script> >>>>>> import numpy as np >>>>>> import scipy.linalg as npl >>>>>> >>>>>> M = 40 >>>>>> N = 10 >>>>>> >>>>>> def make_deficient(): >>>>>> X = np.random.normal(size=(M, N)) >>>>>> deficient_X = X.copy() >>>>>> if M > N: # Make a column deficient >>>>>> deficient_X[:, 0] = deficient_X[:, 1] + deficient_X[:, 2] >>>>>> else: # Make a row deficient >>>>>> deficient_X[0] = deficient_X[1] + deficient_X[2] >>>>>> return deficient_X >>>>>> >>>>>> matrices = [] >>>>>> ranks = [] >>>>>> ranks_inf = [] >>>>>> ranks_ub_inf = [] >>>>>> ranks_ub2_inf = [] >>>>>> ranks_mlab = [] >>>>>> tols = np.zeros((1000, 6)) >>>>>> for i in range(1000): >>>>>> m = make_deficient() >>>>>> matrices.append(m) >>>>>> # The SVD tolerances >>>>>> S = npl.svd(m, compute_uv=False) >>>>>> S0 = S.max() >>>>>> # u in Golub and Van Loan == numpy eps / 2 >>>>>> eps = np.finfo(m.dtype).eps >>>>>> u = eps / 2 >>>>>> # Current numpy matrix_rank algorithm >>>>>> ranks.append(np.linalg.matrix_rank(m)) >>>>>> # Which is the same as: >>>>>> tol_s0 = S0 * eps >>>>>> # ranks.append(np.linalg.matrix_rank(m, tol=tol_s0)) >>>>>> # Golub and Van Loan suggestion >>>>>> tol_inf = npl.norm(m, np.inf) * u >>>>>> ranks_inf.append(np.linalg.matrix_rank(m, tol=tol_inf)) >>>>>> # Upper bound of |X|_{inf} >>>>>> tol_ub_inf = tol_s0 * np.sqrt(N) / 2 >>>>>> ranks_ub_inf.append(np.linalg.matrix_rank(m, tol=tol_ub_inf)) >>>>>> # Times 2 fudge >>>>>> tol_ub2_inf = tol_s0 * np.sqrt(N) >>>>>> ranks_ub2_inf.append(np.linalg.matrix_rank(m, tol=tol_ub2_inf)) >>>>>> # MATLAB algorithm >>>>>> tol_mlab = tol_s0 * max(m.shape) >>>>>> ranks_mlab.append(np.linalg.matrix_rank(m, tol=tol_mlab)) >>>>>> # Collect tols >>>>>> tols[i] = tol_s0, tol_inf, tol_ub_inf, tol_ub2_inf, tol_mlab, S.min() >>>>>> >>>>>> rel_tols = tols / tols[:, -1][:, None] >>>>>> >>>>>> fmt = 'Percent undetected %s: %3.1f, tol / S.min(): %2.3f' >>>>>> max_rank = min(M, N) >>>>>> for name, ranks, mrt in zip( >>>>>> ('current', 'inf norm', 'upper bound inf norm', >>>>>> 'upper bound inf norm * 2', 'MATLAB'), >>>>>> (ranks, ranks_inf, ranks_ub_inf, ranks_ub2_inf, ranks_mlab), >>>>>> rel_tols.mean(axis=0)[:5]): >>>>>> pcnt = np.sum(np.array(ranks) == max_rank) / 1000. * 100 >>>>>> print fmt % (name, pcnt, mrt) >>>>>> </script> >>>>> >>>>> >>>>> The polynomial fitting uses eps times the largest array dimension for the >>>>> relative condition number. IIRC, that choice traces back to numerical >>>>> recipes. >>> >>> Chuck - sorry - I didn't understand what you were saying, and now I >>> think you were proposing the MATLAB algorithm. I can't find that in >>> Numerical Recipes - can you? It would be helpful as a reference. >>> >>>> This is the same as Matlab, right? >>> >>> Yes, I believe so, i.e: >>> >>> tol = S.max() * np.finfo(M.dtype).eps * max((m, n)) >>> >>> from my original email. >>> >>>> If the Matlab condition is the most conservative, then it seems like a >>>> reasonable choice -- conservative is good so long as your false >>>> positive rate doesn't become to high, and presumably Matlab has enough >>>> user experience to know whether the false positive rate is too high. >>> >>> Are we agreeing to go for the Matlab algorithm? >>> >>> If so, how should this be managed? Just changing it may change the >>> output of code using numpy >= 1.5.0, but then again, the threshold is >>> probably incorrect. >>> >>> Fix and break or FutureWarning with something like: >>> >>> def matrix_rank(M, tol=None): >>> >>> where ``tol`` can be a string like ``maxdim``? >> >> I dunno, I don't think we should do a big deprecation dance for every >> bug fix. Is this a bug fix, so numpy will simply start producing more >> accurate results on a given problem? I guess there isn't really a >> right answer here (though claiming that [a, b, a+b] is full-rank is >> clearly broken, and the matlab algorithm seems reasonable for >> answering the specific question of whether a matrix is full rank), so >> we'll have to hope some users speak up... > >I don't see a problem changing this as a bugfix. >statsmodels still has, I think, the original scipy.stats.models >version for rank which is still much higher for any non-huge array and >float, cond=1.0e-12. > >Josef + 1 for making the default "matlab" : it sounds like it would be the least confusing. It also seems to me that a bug fix is probably right procedure. Last, I like best having only the matlab default (options seem uncessary). cheers JB

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moving forward around ABI/API compatibilities (was numpy 1.7.x branch)
by David Cournapeau 26 Jun '12

26 Jun '12

Hi, I am just continuing the discussion around ABI/API, the technical side of things that is, as this is unrelated to 1.7.x. release. On Tue, Jun 26, 2012 at 11:41 AM, Dag Sverre Seljebotn <d.s.seljebotn(a)astro.uio.no> wrote: > On 06/26/2012 11:58 AM, David Cournapeau wrote: >> On Tue, Jun 26, 2012 at 10:27 AM, Dag Sverre Seljebotn >> <d.s.seljebotn(a)astro.uio.no> wrote: >>> On 06/26/2012 05:35 AM, David Cournapeau wrote: >>>> On Tue, Jun 26, 2012 at 4:10 AM, Ondřej Čertík<ondrej.certik(a)gmail.com> wrote: >>>> >>>>> >>>>> My understanding is that Travis is simply trying to stress "We have to >>>>> think about the implications of our changes on existing users." and >>>>> also that little changes (with the best intentions!) that however mean >>>>> either a breakage or confusion for users (due to historical reasons) >>>>> should be avoided if possible. And I very strongly feel the same way. >>>>> And I think that most people on this list do as well. >>>> >>>> I think Travis is more concerned about API than ABI changes (in that >>>> example for 1.4, the ABI breakage was caused by a change that was >>>> pushed by Travis IIRC). >>>> >>>> The relative importance of API vs ABI is a tough one: I think ABI >>>> breakage is as bad as API breakage (but matter in different >>>> circumstances), but it is hard to improve the situation around our ABI >>>> without changing the API (especially everything around macros and >>>> publicly accessible structures). Changing this is politically >>> >>> But I think it is *possible* to get to a situation where ABI isn't >>> broken without changing API. I have posted such a proposal. >>> If one uses the kind of C-level duck typing I describe in the link >>> below, one would do >>> >>> typedef PyObject PyArrayObject; >>> >>> typedef struct { >>> ... >>> } NumPyArray; /* used to be PyArrayObject */ >> >> Maybe we're just in violent agreement, but whatever ends up being used >> would require to change the *current* C API, right ? If one wants to > > Accessing arr->dims[i] directly would need to change. But that's been > discouraged for a long time. By "API" I meant access through the macros. > > One of the changes under discussion here is to change PyArray_SHAPE from > a macro that accepts both PyObject* and PyArrayObject* to a function > that only accepts PyArrayObject* (hence breakage). I'm saying that under > my proposal, assuming I or somebody else can find the time to implement > it under, you can both make it a function and have it accept both > PyObject* and PyArrayObject* (since they are the same), undoing the > breakage but allowing to hide the ABI. > > (It doesn't give you full flexibility in ABI, it does require that you > somewhere have an "npy_intp dims[nd]" with the same lifetime as your > object, etc., but I don't consider that a big disadvantage). > >> allow for changes in our structures more freely, we have to hide them >> from the headers, which means breaking the code that depends on the >> structure binary layout. Any code that access those directly will need >> to be changed. >> >> There is the particular issue of iterator, which seem quite difficult >> to make "ABI-safe" without losing significant performance. > > I don't agree (for some meanings of "ABI-safe"). You can export the data > (dataptr/shape/strides) through the ABI, then the iterator uses these in > whatever way it wishes consumer-side. Sort of like PEP 3118 without the > performance degradation. The only sane way IMO of doing iteration is > building it into the consumer anyway. (I have not read the whole cython discussion yet) What do you mean by "building iteration in the consumer" ? My understanding is that any data export would be done through a level of indirection (dataptr/shape/strides). Conceptually, I can't see how one could keep ABI without that level of indirection without some compile. In the case of iterator, that means multiple pointer chasing per sample -- i.e. the tight loop issue you mentioned earlier for PyArray_DATA is the common case for iterator. I can only see two ways of doing fast (special casing) iteration: compile-time special casing or runtime optimization. Compile-time requires access to the internals (even if one were to use C++ with advanced template magic ala STL/iterator, I don't think one can get performance if everything is not in the headers, but maybe C++ compilers are super smart those days in ways I can't comprehend). I would think runtime is the long-term solution, but that's far away, David

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Semantics of index arrays and a request to fix the user guide
by srean 26 Jun '12

26 Jun '12

>From the user guide: ----------------------------- > Boolean arrays must be of the same shape as the array being indexed, > or broadcastable to the same shape. In the most straightforward case, > the boolean array has the same shape. Comment: So far so good, but the doc has not told me yet what is the shape or the output. -------------- user guide continues with an example: ------------------------------------------------------ > The result is a 1-D array containing all the elements in the indexed array corresponding to all the true elements in the boolean array. Comment: -------------- Now it is not clear from that line whether the shape of the result is generally true or is it specific to the example. So the reader (me) is still confused. User Guide continues: -------------------------------- > With broadcasting, multidimensional arrays may be the result. For example... Comment: -------------- I will get to the example in a minute, but there is no explanation of the mechanism used to arrive at the output shape, is it the shape of what the index array was broadcasted to ? or is it something else, if it is the latter, what is it. Example ------------ The example indexes a (5,7) array with a (5,) index array. Now this very confusing because it seems to contradict the original documentation because (5,) is neither the same shape as (5,7) nor is it broadcastable to it. The steps of the conventional broaddcasting would yield (5,7) (5,) then (5,7) (1,5) then an error because 7 and 5 dont match. User guide continues: ------------------------------ > Combining index arrays with slices. > In effect, the slice is converted to an index array > np.array([[1,2]]) (shape (1,2)) that is broadcast with > the index array to produce a resultant array of shape (3,2). comment: ------------- Here the two arrays have shape (3,) and (1,2) so how does broadcasting yield the shape 3,2. Broadcasting is supposed to proceed trailing dimension first but it seems in these examples it is doing the opposite. ===== So could someone explain the semantics and make the user guide more precise. Assuming the user guide will be the first document the new user will read it is surprisingly difficult to read, primarily because it gets into advanced topics to soon and partially because of ambiguous language. The numpy reference on the other hand is very clear as is Travis's book which I am glad to say I actually bought a long time ago. Thanks, srean

1 1

numpy bug with ndarray subclassing
by Dmitrey 25 Jun '12

25 Jun '12

I will use walkaround but I think you'd better fix the numpy bug: from numpy import ndarray, float64, asanyarray, array class asdf(ndarray): __array_priority__ = 10 def __new__(self, vals1, vals2): obj = asanyarray(vals1).view(self) obj.vals2 = vals2 return obj def __add__(self, other): print('add') assert not isinstance(other , asdf), 'unimplemented' return asdf(self.view(ndarray) + other, self.vals2) def __radd__(self, other): print('radd') assert not isinstance(other , asdf), 'unimplemented' return asdf(self.view(ndarray) + other, self.vals2) a = asdf(array((1, 2, 3)), array((10, 20, 30))) z = float64(1.0) print(a.__array_priority__) # 10 print(z.__array_priority__) # -1000000.0 r2 = a + z print(r2.vals2) # ok, prints 'add' and (10,20,30) r1 = z+a print(r1.vals2) # doesn't print "radd" (i.e. doesn't enters asdf.__radd__ function at all) # raises AttributeError #"'asdf' object has no attribute 'vals2'" tried in Python2 + numpy 1.6.1 and Python3 + numpy 1.7.0 dev

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Is IPython useful for your research/industry work? Feedback wanted for grant proposal.
by Fernando Perez 22 Jun '12

22 Jun '12

Hi folks, sorry for the cross-post, but I expect all replies to this to happen off-list. I'm in the process of writing an NSF grant that will partly include IPython support, and along with Brian we will soon be doing more of the same. In the past we haven't had the best of luck with the NFS, hopefully this time it will be better. I think one mistake we've made has been to have very little in the way of hard evidence of the value (if any) that IPython provides to the scientific work of others and to industry. So I would greatly appreciate if you can contact me off-list (best at Fernando.Perez(a)berkeley.edu) with any info that I could use in a typical NSF grant application. I'm not looking for marketing-type testimonials nor letters of support (the NSF frowns on those), but rather specific info, best if backed by journal citations, on how and where IPython plays an important role in your research or industry project (while the NSF is a science funding agency, it also has as part of its mission the economic well-being of the US). I'd also like to clarify that I'm not looking for quotes strictly of personal use as an interactive shell, since I know in this community most people do that. What I'm after are things like: - a research project that builds on IPython in some capacity - important results obtained with the IPython parallel machinery that were better/easier/whatever than a classical approach with other tools - uses of the notebook in education - anything else along these lines you can think of, that goes beyond pure personal shell use. Thanks! Again, in the interest of keeping list noise down, please reply directly to me: Fernando.Perez(a)berkeley.edu. f

1 0

Building numpy with MSVC 2010 and Intel Fortran 10.x (and higher)
by KACVINSKY Tom 22 Jun '12

22 Jun '12

I have reason to build Python 2.6.8 and numpy 1.4.1 with MSVC 2010 and Intel Fortran 10.1 (and higher). I also am building with MKL 10.3. So far, I am able to get the setup to recognize the MKL libraries: C:\Users\tky\Python\numpy-1.6.2>python setup.py build --compiler=msvc --fcompiler=intel Running from numpy source directory.Forcing DISTUTILS_USE_SDK=1 F2PY Version 2 blas_opt_info: blas_mkl_info: FOUND: libraries = ['mkl_sequential_dll', 'mkl_intel_lp64_dll', 'mkl_core_dll'] library_dirs = ['u:\\users\\tky\\mkllib'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['u:\\users\\tky\\mklinc'] FOUND: libraries = ['mkl_sequential_dll', 'mkl_intel_lp64_dll', 'mkl_core_dll'] library_dirs = ['u:\\users\\tky\\mkllib'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['u:\\users\\tky\\mklinc'] lapack_opt_info: lapack_mkl_info: mkl_info: FOUND: libraries = ['mkl_sequential_dll', 'mkl_intel_lp64_dll', 'mkl_core_dll'] library_dirs = ['u:\\users\\tky\\mkllib'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['u:\\users\\tky\\mklinc'] FOUND: libraries = ['mkl_sequential_dll mkl_intel_lp64_dll mkl_core_dll', 'mkl_sequential_dll', 'mkl_intel_lp64_dll', 'mkl_core_dll'] library_dirs = ['u:\\users\\tky\\mkllib'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['u:\\users\\tky\\mklinc'] FOUND: libraries = ['mkl_sequential_dll mkl_intel_lp64_dll mkl_core_dll', 'mkl_sequential_dll', 'mkl_intel_lp64_dll', 'mkl_core_dll'] library_dirs = ['u:\\users\\tky\\mkllib'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['u:\\users\\tky\\mklinc'] But where things are failing is during the compiler checks: customize IntelFCompiler Found executable C:\Program Files (x86)\Intel\Compiler\Fortran\10.1.011\EM64T\bin\ifort.exe Found executable C:\Program Files (x86)\Intel\Compiler\Fortran\10.1.011\EM64T\bin\ifort.exe Traceback (most recent call last): File "setup.py", line 214, in <module> setup_package() File "setup.py", line 207, in setup_package configuration=configuration ) File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\core.py", line 186, in setup return old_setup(**new_attr) File "C:\Users\tky\Python\release\lib\distutils\core.py", line 152, in setup dist.run_commands() File "C:\Users\tky\Python\release\lib\distutils\dist.py", line 975, in run_commands self.run_command(cmd) File "C:\Users\tky\Python\release\lib\distutils\dist.py", line 995, in run_command cmd_obj.run() File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\command\build.py", line 37, in run old_build.run(self) File "C:\Users\tky\Python\release\lib\distutils\command\build.py", line 134, in run self.run_command(cmd_name) File "C:\Users\tky\Python\release\lib\distutils\cmd.py", line 333, in run_command self.distribution.run_command(command) File "C:\Users\tky\Python\release\lib\distutils\dist.py", line 995, in run_command cmd_obj.run() File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\command\build_src.py", line 152, in run self.build_sources() File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\command\build_src.py", line 163, in build_sources self.build_library_sources(*libname_info) File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\command\build_src.py", line 298, in build_library_sources sources = self.generate_sources(sources, (lib_name, build_info)) File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\command\build_src.py", line 385, in generate_sources source = func(extension, build_dir) File "numpy\core\setup.py", line 694, in get_mathlib_info st = config_cmd.try_link('int main(void) { return 0;}') File "C:\Users\tky\Python\release\lib\distutils\command\config.py", line 257, in try_link self._check_compiler() File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\command\config.py", line 77, in _check_compiler self.fcompiler.customize(self.distribution) File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\fcompiler\__init__.py", line 502, in customize get_flags('arch', aflags) File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\fcompiler\__init__.py", line 491, in get_flags flags.extend(getattr(self.flag_vars, tag)) File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\environment.py", line 37, in __getattr__ return self._get_var(name, conf_desc) File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\environment.py", line 51, in _get_var var = self._hook_handler(name, hook) File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\fcompiler\__init__.py", line 698, in _environment_hook return hook() File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\fcompiler\intel.py", line 63, in get_flags_arch v = self.get_version() File "C:\Users\tky\Python\numpy-1.6.2\numpy\distutils\fcompiler\__init__.py", line 432, in get_version raise CompilerNotFound() numpy.distutils.fcompiler.CompilerNotFound Any ideas on how to circumvent this? Thanks, Tom 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

2 3

Good way to develop numpy as popular choice!
by bob tnur 22 Jun '12

22 Jun '12

Hi all numpy fun;) This question is already posted in stackoverflow by some people, I am just thinking that numpy python will do this with trick;) I guess numpy will be every ones choice as its popularity increases. The question is herein: http://stackoverflow.com/questions/10074270/how-can-i-find-the-minimum-numb… Have fun with numpy:) Bob

6 11

"can cast safely" table
by Pierre Haessig 21 Jun '12

21 Jun '12

Hi, While getting through the ufunc documentation, (http://docs.scipy.org/numpy/docs/numpy-docs/reference/ufuncs.rst/) I took the liberty to change one line in the code segment which generates the "can cast safely" table. I wanted to increase the readability if the table by increasing its contrast. To this end, I replaced the character '0' by '-'. (I copy pasted the result below). Now two questions : 1) One thing is that some of you may not like this change and I'm fine with that because this was just for fun ! 2) A slightly more important thing is to double check that the table content is unchanged. Indeed, the table caption says it was generated on a 32 bits machine while my computer is running a 64 bits Linux. I believe that the table I generated was the same, but please make sure I didn't mess up something. Best, Pierre Quick preview of the change : the "can cast safely" table before : X ? b h i l q p B H I L Q P e f d g F D G S U V O M m ? 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 b 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 h 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 i 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0 l 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0 q 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0 p 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0 B 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 H 0 0 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 I 0 0 0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 L 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 Q 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 P 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 e 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 f 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 d 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0 g 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 F 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 D 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 G 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 S 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 U 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 V 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 O 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 M 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 m 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 the "can cast safely" table after : X ? b h i l q p B H I L Q P e f d g F D G S U V O M m ? 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 b - 1 1 1 1 1 1 - - - - - - 1 1 1 1 1 1 1 1 1 1 1 - - h - - 1 1 1 1 1 - - - - - - - 1 1 1 1 1 1 1 1 1 1 - - i - - - 1 1 1 1 - - - - - - - - 1 1 - 1 1 1 1 1 1 - - l - - - - 1 1 1 - - - - - - - - 1 1 - 1 1 1 1 1 1 - - q - - - - 1 1 1 - - - - - - - - 1 1 - 1 1 1 1 1 1 - - p - - - - 1 1 1 - - - - - - - - 1 1 - 1 1 1 1 1 1 - - B - - 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 - - H - - - 1 1 1 1 - 1 1 1 1 1 - 1 1 1 1 1 1 1 1 1 1 - - I - - - - 1 1 1 - - 1 1 1 1 - - 1 1 - 1 1 1 1 1 1 - - L - - - - - - - - - - 1 1 1 - - 1 1 - 1 1 1 1 1 1 - - Q - - - - - - - - - - 1 1 1 - - 1 1 - 1 1 1 1 1 1 - - P - - - - - - - - - - 1 1 1 - - 1 1 - 1 1 1 1 1 1 - - e - - - - - - - - - - - - - 1 1 1 1 1 1 1 1 1 1 1 - - f - - - - - - - - - - - - - - 1 1 1 1 1 1 1 1 1 1 - - d - - - - - - - - - - - - - - - 1 1 - 1 1 1 1 1 1 - - g - - - - - - - - - - - - - - - - 1 - - 1 1 1 1 1 - - F - - - - - - - - - - - - - - - - - 1 1 1 1 1 1 1 - - D - - - - - - - - - - - - - - - - - - 1 1 1 1 1 1 - - G - - - - - - - - - - - - - - - - - - - 1 1 1 1 1 - - S - - - - - - - - - - - - - - - - - - - - 1 1 1 1 - - U - - - - - - - - - - - - - - - - - - - - - 1 1 1 - - V - - - - - - - - - - - - - - - - - - - - - - 1 1 - - O - - - - - - - - - - - - - - - - - - - - - - 1 1 - - M - - - - - - - - - - - - - - - - - - - - - - - - 1 - m - - - - - - - - - - - - - - - - - - - - - - - - - 1

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trivial question?
by Neal Becker 21 Jun '12

21 Jun '12

Maybe I'm being slow, but is there any convenient function to calculate, for 2 vectors: \sum_i \sum_j x_i y_j (I had a matrix once, but it vanished without a trace)

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