Re: [Numpy-discussion] segfault with dot
What CPU do you have and which version of numpy did you get and where did you get it from ? Matthieu 2007/11/17, Jesus Torrecilla Pinero [EMAIL PROTECTED]: I am using Python 2.5.1 (r251:54863, Apr 18 2007, 08:51:08) under Windows XP and converting a program from Numeric to Numpy. If I have two arrays, say K and T and do dot(K,T) or K*T everything goes well, but if I have a vector b and try dot(K,b) or K*b I get a segfault. I have tried to run the test in tes_numeric.py and get the same result. I have tried too to convert b in a matrix with two columns, the second one being all zeroes, and numpy makes the multiplication correctly, so I think the problem is just with vectors Does this happens with other versions or do you have any idea to fix this problem? Thanks in advance -- Jesús Torrecilla Pinero Universidad de Extremadura jtorrecilla at utcsl.com jtorreci at unex.es ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion -- French PhD student Website : http://miles.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] SegFault/double free with simple array mask operation
Achim Gaedke wrote:
David Cournapeau wrote:
Could you open a ticket on the numpy trac system ? (I can confirm the bug)
cheers,
David
It is Ticket #614 . The version information in trac are outdated, I
could not select version 1.0.3 or 1.0.4 .
Here is the solution for Segmentation Fault reported.
It is basicly copied from the function iter_subscript_Bool, which alredy
does the necessary range checks.
Achim
Index: arrayobject.c
===
--- arrayobject.c (revision 4464)
+++ arrayobject.c (working copy)
@@ -9337,6 +9337,11 @@
return -1;
}
index = ind-dimensions[0];
+if (index self-size) {
+PyErr_SetString(PyExc_ValueError,
+too many boolean indices);
+return -1;
+}
strides = ind-strides[0];
dptr = ind-data;
PyArray_ITER_RESET(self);
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Re: [Numpy-discussion] segfault with dot
numpy-1.0.4.win32-py2.5 from sourceforge (precompiled binary) CPU: AMD Athlon XP 2600+ 2.09 GHz, 1.00 Gb RAM The error report says: AppName: pythonw.exe AppVer: 0.0.0.0 ModName: _dotblas.pyd ModVer: 0.0.0.0 Offset: 0007ecf3 Jesús Torrecilla Pinero ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] segfault with dot
2007/11/17, Jesus Torrecilla Pinero [EMAIL PROTECTED]: numpy-1.0.4.win32-py2.5 from sourceforge (precompiled binary) CPU: AMD Athlon XP 2600+ 2.09 GHz, 1.00 Gb RAM The error report says: AppName: pythonw.exe AppVer: 0.0.0.0 ModName: _dotblas.pyd ModVer: 0.0.0.0 Offset: 0007ecf3 OK, I think the problem is that you don't have the SSE2 instruction set and IIRC, this package needs it. David Cournapeau created a package that could solve your problem, but I don't remember the link, you can brows the archive for it ;) Matthieu -- French PhD student Website : http://miles.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy : your experiences?
On Sat, Nov 17, 2007 at 02:07:34AM -0500, Anne Archibald wrote: On 16/11/2007, Rahul Garg [EMAIL PROTECTED] wrote: It would be awesome if you guys could respond to some of the following questions : a) Can you guys tell me briefly about the kind of problems you are tackling with numpy and scipy? b) Have you ever felt that numpy/scipy was slow and had to switch to C/C++/Fortran? c) Do you use any form of parallel processing? Multicores? SMPs? Clusters? If yes how did u utilize them? If you feel its not relevant to the list .. feel free to email me personally. I would be very interested in talking about these issues. I think it would be interesting and on-topic to hear a few words from people to see what they do with numpy. a) I use python/numpy/scipy to work with astronomical observations of pulsars. This includes a range of tasks including: simple scripting to manage jobs on our computation cluster; minor calculations (like a better scientific calculator, though frink is sometimes better because it keeps track of units); So does 'ipython -p physics': In [1]: x = 3 m/s^2 In [2]: y = 15 s In [3]: x*y Out[3]: 45 m/s Regards Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [ANN] Release of the first PyTables video
Hi, = Release of the first PyTables video = `Carabos http://www.carabos.com/`_ is very proud to announce the first of a series of videos dedicated to introducing the main features of PyTables to the public in a visual and easy to grasp manner. I just got a chance to watch the video and wanted to thank you for putting that together. I've always been meaning to check out PyTables but haven't had the time to figure out how to work it on to potentially replace my hacked- together data storage schemes, so these videos are a great help. Looking forward to your next video! -steve ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy : your experiences?
Hi Rahul, a) Can you guys tell me briefly about the kind of problems you are tackling with numpy and scipy? I'm a grad student doing computational biology. I primarily use the NumPy/SciPy/matplotlib triumvirate as a post processing tool to analyze what the heck happened after we run some learning algorithms we develop (or canned ones, like libsvm (for example)) to look for some sense in the results. I've been working w/ analyzing interaction networks/graphs, so I also use NetworkX[1] quite a bit as well (it's also a nice package w/ responsive authors). Many of the folks (in my lab, and collaborators) like to use MATLAB, so I've found scipy's io.loadmat invaluable for making this a bit more seamless. So, in general, for me (so far) numpy/scipy are generally used to integrate various datasets together and see if things look kosher (before runs and after runs). b) Have you ever felt that numpy/scipy was slow and had to switch to C/C++/Fortran? Yes, for things like boosting, svm, graph mining, etc ... but that's no real surprise since their iterative and need to run on large datasets. You should also note that there are python interfaces to these things out there as well, but I (thus far) haven't taken much of advantage of those and usually pipe out data into the expected text input formats and pull them back in when the algo is done. c) Do you use any form of parallel processing? Multicores? SMPs? Clusters? If yes how did u utilize them? I'd really like to (not just for Python), but I haven't. -steve [1] NetworkX: https://networkx.lanl.gov/wiki ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] bug in numpy.apply_along_axis: numpy.__version__ '1.0.3.1'
Hi, I think I have found a bug in the function apply_along_axis. recall that the function definition if apply_along_axis(func1d, axis, arr, *args) This bug occurs if func1d returns a python object without a __len__ attribute and is not a scalar, determined by numpy.core.numeric.isscalar. eg. a self contained example of the bug is: from numpy import array, apply_along_axis # works arr = array( [1,1] ) apply_along_axis( lambda arr:arr[0], 0, arr ) # however this raises a type error class Foo(object): pass arr = array( [ Foo(), Foo() ] ) apply_along_axis( lambda arr:arr[0], 0, arr ) --- exceptions.TypeError Traceback (most recent call last) /sw/lib/python2.4/site-packages/numpy/lib/shape_base.py in apply_along_axis(func1d, axis, arr, *args) 52 holdshape = outshape 53 outshape = list(arr.shape) --- 54 outshape[axis] = len(res) 55 outarr = zeros(outshape,asarray(res).dtype) 56 outarr[tuple(i.tolist())] = res TypeError: len() of unsized object Thanks ~Sean___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Bug in arange dtype f was: Using arr.dtype.type to check byteorder-independed dtype fails for bool
On Tue, Nov 13, 2007 at 02:53:32PM +0100, Sebastian Haase wrote:
On Nov 13, 2007 2:18 PM, Stefan van der Walt [EMAIL PROTECTED] wrote:
Hi Sebastian
On Tue, Nov 13, 2007 at 01:11:33PM +0100, Sebastian Haase wrote:
Hi,
I need to check the array dtype in a way that it is ignoring
differences coming only from big-endian vs. little-endian.
Does
N.issubdtype(first_dtype, second_dtype)
work?
Hi Stéfan,
trying to anwer your question with a quick arange test, I ran into
more confusion:
a = N.arange(.5, dtype=f)
`a.dtype`
'dtype('float32')'
a = N.arange(.5, dtype=f)
`a.dtype`
'dtype('float32')'
Both equal positively with N.float32 now !
N.__version__
'1.0.1'
This should now be fixed in SVN r4465.
Regards
Stéfan
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[Numpy-discussion] Should array iterate over a set?
My expectation was that array would iterate over a set. This is incorrect: array(set([1,2,3])) array(set([1, 2, 3]), dtype=object) Is this the intended behaviour? A trivial work-around that does what I need is array(list(set([1,2,3]))) array([1, 2, 3]) but I was wondering if this was by design or just a forgotten corner. (Maybe a vestige of the tuple special case for record arrays?) Michael. P.S. I just found that this was brought up by Ed Schofield on 2006-05-03, but there were no replies in that thread. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Should array iterate over a set?
Michael McNeil Forbes wrote: My expectation was that array would iterate over a set. This is incorrect: array(set([1,2,3])) array(set([1, 2, 3]), dtype=object) Is this the intended behaviour? A trivial work-around that does what I need is array(list(set([1,2,3]))) array([1, 2, 3]) but I was wondering if this was by design or just a forgotten corner. (Maybe a vestige of the tuple special case for record arrays?) We can recognize most sequences (i.e. for all i in range(len(x)), x[i] responds correctly), but we cannot easily deal with arbitrary iterables which are not sequences in the array() function. There are a lot of special cases and heuristics going on in array() as it is. Instead, we have a fromiter() function which will take an iterable and construct an array from it. It is limited to 1D arrays, but this is by far the most common use for constructing an array from an iterable. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
