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From: Travis O. <oli...@ie...> - 2006-08-11 01:45:16
|
Sebastian Haase wrote: > Hi, > Does numpy.ascontiguousarray(arr) "fix" the byteorder when arr is non-native > byteorder ? > > If not, what functions does ? > It can if you pass in a data-type with the right byteorder (or use a native built-in data-type). In NumPy, it's the data-type that carries the "byte-order" information. So, there are lot's of ways to "fix" the byte-order. Of course there is still the difference between "fixing" the byte-order and simply "viewing" the memory in the correct byte-order. The former physically flips bytes around, the latter just flips them on calculation and presentation. -Travis |
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From: <hj...@to...> - 2006-08-11 01:34:15
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From: Sebastian H. <ha...@ms...> - 2006-08-11 00:42:54
|
Hi, Does numpy.ascontiguousarray(arr) "fix" the byteorder when arr is non-native byteorder ? If not, what functions does ? - Sebastian Haase |
|
From: Sebastian H. <ha...@ms...> - 2006-08-11 00:23:12
|
On Thursday 10 August 2006 16:57, Travis Oliphant wrote:
> Sebastian Haase wrote:
> > Hi,
> > trying to convert my memmap - records - numarray code for reading a
> > image file format (Mrc).
> > There are 10 fields of strings (each 80 chars long) in the header:
> > in numarray I used the format string '10a80'
> > This results in a single <None> value in numpy.
> > Same after changing it to '10S80'.
> >
> > Am I doing something wrong !?
>
> Not that I can see. But, it's possible that there is a
> misunderstanding of what '10a80' represents.
>
> What is giving you the <None> value?
>
> For example, I can create a file with 10, 80-character strings and open it
> using memmap and a data-type of
>
> dt = numpy.dtype('10a80')
>
> and it seems to work fine.
>
> -Travis
This is what I get: It claims that the 'title' field (the last one) is 10
times 'S80' but trying to read that array from the first (and only) record
(a.Mrc._hdrArray.title[0]) I just get None...
>>> a=Mrc.bindFile('Heather2/GFPtublive-Vecta43')
TODO: byteorder
>>> repr(a.Mrc._hdrArray.dtype)
'dtype([('Num', '<i4', 3), ('PixelType', '<i4'), ('mst', '<i4', 3), ('m',
'<i4', 3), ('d', '<f4', 3), ('angle', '<f4', 3), ('axis', '<i4', 3), ('mmm1',
'<f4', 3), ('type', '<i2'), ('nspg', '<i2'), ('next', '<i4'), ('dvid',
'<i2'), ('blank', '|i1', 30), ('NumIntegers', '<i2'), ('NumFloats', '<i2'),
('sub', '<i2'), ('zfac', '<i2'), ('mm2', '<f4', 2), ('mm3', '<f4', 2),
('mm4', '<f4', 2), ('ImageType', '<i2'), ('LensNum', '<i2'), ('n1', '<i2'),
('n2', '<i2'), ('v1', '<i2'), ('v2', '<i2'), ('mm5', '<f4', 2), ('NumTimes',
'<i2'), ('ImgSequence', '<i2'), ('tilt', '<f4', 3), ('NumWaves', '<i2'),
('wave', '<i2', 5), ('zxy0', '<f4', 3), ('NumTitles', '<i4'), ('title', '|
S80', 10)])'
>>> a.Mrc._hdrArray.NumTitles
[3]
>>> a.Mrc._hdrArray.NumTitles[0]
3
>>> type(a.Mrc._hdrArray.title[0])
<type 'NoneType'>
>>> type(a.Mrc._hdrArray.title[1])
Traceback (most recent call last):
File "<input>", line 1, in ?
File "/home/haase/qqq/lib/python/numpy/core/defchararray.py", line 45, in
__getitem__
val = ndarray.__getitem__(self, obj)
IndexError: index out of bounds
I get the same on byteswapped data and non-byteswapped data.
-Sebastian
|
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From: Sebastian H. <ha...@ms...> - 2006-08-10 23:43:12
|
Hi, trying to convert my memmap - records - numarray code for reading a image file format (Mrc). There are 10 fields of strings (each 80 chars long) in the header: in numarray I used the format string '10a80' This results in a single <None> value in numpy. Same after changing it to '10S80'. Am I doing something wrong !? Thanks, Sebastian Haase |
|
From: Travis O. <oli...@ie...> - 2006-08-10 20:22:26
|
Sasha wrote: > I see that Travis just fixed that by making context optional > <http://projects.scipy.org/scipy/numpy/changeset/2987>. I am not sure > it is a good idea to allow use of ufuncs for which domain is not > defined in ma. This may lead to hard to find bugs coming from ma > arrays with nans in the data. I would rather see linalg passing the > (func,args) context to wrap. That would not fix the reported problem, > but will make diagnostic clearer. > > This can be done as well. The problem is that __array_wrap__ is used in quite a few places (without context) and ma needs to have a default behavior when context is not supplied. -Travis |
|
From: Sasha <nd...@ma...> - 2006-08-10 20:07:19
|
I see that Travis just fixed that by making context optional <http://projects.scipy.org/scipy/numpy/changeset/2987>. I am not sure it is a good idea to allow use of ufuncs for which domain is not defined in ma. This may lead to hard to find bugs coming from ma arrays with nans in the data. I would rather see linalg passing the (func,args) context to wrap. That would not fix the reported problem, but will make diagnostic clearer. On 8/10/06, Daran L. Rife <dr...@uc...> wrote: > Hi Sasha, > > > Inverting a matrix with masked values does not make much sense. Call > > "filled" method with an appropriate fill value before passing the > > matrix to "inv". > > In principle you are right, but even though I use masked arrays > in this operation, when the operation itself is done no masked > values remain. Thus, my code works very well with the "old" > Numeric--and has worked well for some time. That said, I will > try your suggestion of doing a "filled" on the matrix before > sending it off to the inverse module. > > > Thanks, > > > Daran > > ------------------------------------------------------------------------- > Using Tomcat but need to do more? Need to support web services, security? > Get stuff done quickly with pre-integrated technology to make your job easier > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 > _______________________________________________ > Numpy-discussion mailing list > Num...@li... > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > |
|
From: Daran L. R. <dr...@uc...> - 2006-08-10 19:33:59
|
Hi Sasha, > Inverting a matrix with masked values does not make much sense. Call > "filled" method with an appropriate fill value before passing the > matrix to "inv". In principle you are right, but even though I use masked arrays in this operation, when the operation itself is done no masked values remain. Thus, my code works very well with the "old" Numeric--and has worked well for some time. That said, I will try your suggestion of doing a "filled" on the matrix before sending it off to the inverse module. Thanks, Daran |
|
From: Travis O. <oli...@ie...> - 2006-08-10 19:10:56
|
I've just finished a first version of the numarray compatibility module. It does not include all the names from the numarray name-space but it does include the most important ones, I believe. It also includes a slightly modified form of the numarray type-objects so that NumPy can recognize them as dtypes. I do not have a lot of code to test the compatibility layer with so any help will be appreciated. The compatibility layer still requires changes to certain methods and attributes on arrays. This is performed by the alter_code1.py module which I will be finishing over the next few hours. Once that is ready (and I've updated NumPy to work with the latest version of Python 2.5 in SVN) I want to make a 1.0b2 release (no later than Friday). I would appreciate it if several people could test the current SVN version of NumPy. In order to support several of the features of NumArray that I had missed, I engaged in a marathon coding sprint last night from about 6:00pm to 6:00am during which time I added output arguments to many of the functions in NumPy, and a clipmode argument to several others. I also added the C-API functions PyArray_OutputConverter and PyArray_ClipmodeConverter to make it easy to get these arguments from Python to C. This caused a change in the C-API that will require re-compilation for 1.0b2. I'm sorry about that. I'm really pushing for stability on the C-API. Now that the numarray compatibility module is complete, I'm more confident that we won't need anymore changes to the C-API for version 1.0. Of course, only when numpy 1.0final comes out will that be a guarantee. While I'm relatively confident about the changes to NumPy, the changes were extensive enough that more testing is warranted including another round of Valgrind tests. Unit-tests written to take advantage of the new output arguments on several of the functions (take, put, compress, clip, conjugate, argmax, argmin, and any function based on a ufunc method -- like sum, product, any, all, etc.) are particularly needed. If serious problems are discovered, then the 1.0b2 might be delayed again, but I'm really pushing to get 1.0b2 out the door soon. The numarray compatibility module and the oldnumeric compatibility module should hopefully help people adapt their code more quickly to NumPy. It's not fool-proof, though, so the best strategy is still to write to NumPy :-) as soon as you can. -Travis |
|
From: Sasha <nd...@ma...> - 2006-08-10 18:41:36
|
Inverting a matrix with masked values does not make much sense. Call "filled" method with an appropriate fill value before passing the matrix to "inv". On 8/10/06, Daran L. Rife <dr...@uc...> wrote: > Hello, > > I am a veteran user of Numeric and am trying > out the latest version of numpy (numpy 1.01b) > on Mac OS X 10.4 Tiger (8.7.0). > > When trying to invert a matrix with > numpy.linalg.inv I get the following error: > > ----> > > Traceback (most recent call last): > File "./bias_correction.py", line 381, in ? > if __name__ == "__main__": main() > File "./bias_correction.py", line 373, in main > (index_to_stnid, bias_and_innov) = calc_bias_and_innov(cf, stn_info, > obs, infile_obs, grids, infile_grids) > File "./bias_correction.py", line 297, in calc_bias_and_innov > K = make_kalman_gain(R, P_local, H) > File "./bias_correction.py", line 157, in make_kalman_gain > K = MA.dot( MA.dot(P, MA.transpose(H)), inv(MA.dot(H, MA.dot(P, > MA.transpose(H))) + R ) ) > File "/opt/python/lib/python2.4/site-packages/numpy/linalg/linalg.py", > line 149, in inv > return wrap(solve(a, identity(a.shape[0], dtype=a.dtype))) > TypeError: __array_wrap__() takes exactly 3 arguments (2 given) > > <---- > > Is this a known problem, and if so, what is the fix? > > > Thanks very much, > > > Daran > > > > > > ------------------------------------------------------------------------- > Using Tomcat but need to do more? Need to support web services, security? > Get stuff done quickly with pre-integrated technology to make your job easier > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 > _______________________________________________ > Numpy-discussion mailing list > Num...@li... > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > |
|
From: David M. C. <co...@ph...> - 2006-08-10 18:22:40
|
On Thu, 10 Aug 2006 07:33:44 -0600 (MDT) "Daran L. Rife" <dr...@uc...> wrote: > Hello, > > I am a veteran user of Numeric and am trying > out the latest version of numpy (numpy 1.01b) > on Mac OS X 10.4 Tiger (8.7.0). > > When trying to invert a matrix with > numpy.linalg.inv I get the following error: > > ----> > > Traceback (most recent call last): > File "./bias_correction.py", line 381, in ? > if __name__ == "__main__": main() > File "./bias_correction.py", line 373, in main > (index_to_stnid, bias_and_innov) = calc_bias_and_innov(cf, stn_info, > obs, infile_obs, grids, infile_grids) > File "./bias_correction.py", line 297, in calc_bias_and_innov > K = make_kalman_gain(R, P_local, H) > File "./bias_correction.py", line 157, in make_kalman_gain > K = MA.dot( MA.dot(P, MA.transpose(H)), inv(MA.dot(H, MA.dot(P, > MA.transpose(H))) + R ) ) > File "/opt/python/lib/python2.4/site-packages/numpy/linalg/linalg.py", > line 149, in inv > return wrap(solve(a, identity(a.shape[0], dtype=a.dtype))) > TypeError: __array_wrap__() takes exactly 3 arguments (2 given) > > <---- > > Is this a known problem, and if so, what is the fix? It looks like the problem is that numpy.core.ma.MaskedArray.__array_map__ expects a "context" argument, but none gets passed. I'm not familiar with that, so I don't know what the fix is ... -- |>|\/|< /--------------------------------------------------------------------------\ |David M. Cooke http://arbutus.physics.mcmaster.ca/dmc/ |co...@ph... |
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From: Bryce H. <bhe...@en...> - 2006-08-10 16:51:24
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For those not able to make SciPy 2006 next week, or who would like to download the ISO a few days early, its available at http://code.enthought.com/downloads/scipy2006-i386.iso.torrent. We squashed a lot onto the CD, so I also had to trim > 100 MB of packages that ship with the standard Ubuntu CD. Here's what I was able to add: * SciPy build from svn (Wed, 12:00 CST) * NumPy built from svn (Wed, 12:00 CST) * Matplotlib built from svn (Wed, 12:00 CST) * IPython built from svn (Wed, 12:00 CST) * Enthought built from svn (Wed, 16:00 CST) * ctypes 1.0.0 * hdf5 1.6.5 * networkx 0.31 * Pyrex 0.9.4.1 * pytables 1.3.2 All of the svn checkouts are zipped in /src, if you'd like to build from a svn version newer than what was shipped, simple copy the compressed package to your home dir, uncompress it, run "svn upate", and built it. Please note: This ISO was built rather hastily, uses un-official code, and received very little testing. Please don't even consider using this in a production environment. Bryce |
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From: Hanno K. <kl...@ph...> - 2006-08-10 16:50:43
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Hi Daran, I fortunately never had the need to run different versions in parallel, so I basically removed the earlier versions of numpy. However, as you possibly know, you can build wrapper functions for fortran code with f2py (which is now shipped with numpy). And that is where I got the segfault behaviour: I had a module compiled for numpy 0.9.6 and then tried to use it with numpy 1.0b. Therefore I thought if you have similar stuff running on your machine that might be a reason. The obvious solution is to recompile the fortran code with the newer version of f2py. But fom what you write, your problem seems to be different. Regards, Hanno "Daran L. Rife" <dr...@uc...> said: > Hi Hanno, > > > I had a similar behaviour when I tried to use module compield with an > > older f2py with a newer version of numpy. So is it maybe possible that > > some *.so files are used from an earlier build? > > > Many thanks for the reply. This was my first attempt > to build and use numpy; I have no previous version. > May I ask how you specificlly solved the problem > on your machine? > > Thanks, > > Daran > > -- > > -- Hanno Klemm kl...@ph... |
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From: Daran L. R. <dr...@uc...> - 2006-08-10 16:36:23
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Hi Chris, Thanks very much for your reply. My apology for the confusion. To be clear, I am a veteran user of Numeric not numpy. I tried installing four versions of Numeric: 23.8, 24.0, 24.1, and 24.2. My Python distro is built from source, using the GCC 4.0.1 suite of compilers. I am running all of this on a Mac G5 PowerPC with Mac OS X 10.4 Tiger (8.7.0). All branches of Numeric 24.x cause a "Segmentation Fault". The scripts I was running this against are a bit complex, so it is not so easy for me to sort out when/where the failure occurs. I'll keep doing some testing and see if I can get a better idea for what seems to be the issue. I'd very like much like to move to numpy, but I have code that needs to be working -now-, so at this point I am more interested in Numeric; I am an adept user of Numeric, and I know it works well on Debian Linux boxes. I will try your suggestion of installing and running the pre-built packages at <http://www.pythonmac.org/packages/py24-fat/>. Thanks again for your patience and for your help. Daran -- > Daran L. Rife wrote: >> Many thanks for the reply. This was my first attempt >> to build and use numpy; > > "numpy" used to be a generic name for the Numerical extensions to > Python. Now there are three versions: > > "Numeric": The original, now at version 24.2 This is probably the last > version that will be produced. > > "numarray": This was designed to be the "next generation" array package. > It has some nice additional features that Numeric does not have, but is > missing some as well. It is at version 1.5.1. it may see some bug fix > releases in the future, but probably won't see any more major development. > > "numpy": this is the "grand unification" array package. It is based on > the Numeric code base, and is designed to have the best features of > Numeric and numarray, plus some extra good stuff. It is now at version > 1.0beta, with an expected release date for 1.0final sometime this fall. > It is under active development, the API is pretty stable now, and it > appears to have the consensus of the numerical python community as the > "way of the future" > > I wrote all that out so that you can be clear which package you are > having trouble with -- you've used both the term "Numeric" and "numpy" > in your posts, and there is some confusion. > > If you are working on a project that does not need to be released for a > few months (i.e. after numpy has reached 1.0 final), I'd use numpy, > rather than Numeric or numarray. > > Also: on OS-X, there are far to many ways to build Python. When you > report a problem, you need to define exactly which python build you are > using, and this goes beyond python version -- fink? darwinports? > built-it-from-source? Framework? Universal, etc... > > The MacPython community is doing it's best to standardize on the > Universal Build of 2.4.3 that you can find here: > > http://www.pythonmac.org/packages/py24-fat/ > > There you will also find pre-built packages for Numeric24.2, > numarray1.5.1, and numpy0.9.8 > > Have you tried any of those? They should be built against Apple's > vectLib. There isn't a package for numpy 1.0beta there yet. I may add > one soon. > >> Interestingly, I can get Numeric version 23.8 to build and >> run just fine, but it appears that the dotblas (BLAS >> optimized matrixmultiply/dot/innerproduct) does not properly >> get built in. Thus, all my matrix operations are -very- slow. > > I'm not sure of the dates, but that is probably a version that didn't > have the check for Apple's vecLib in the setup.py, so it built with the > built-in lapack-lite instead. You can compare the setup.py files from > that and newer versions to see how to make it build against vectLib, but > I suspect if you do that, you'll see the same problems. > > Also, please send a small test script that crashes for you, so others > can test it. > > -Chris |
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From: Christopher B. <Chr...@no...> - 2006-08-10 16:14:12
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Daran L. Rife wrote: > Many thanks for the reply. This was my first attempt > to build and use numpy; "numpy" used to be a generic name for the Numerical extensions to Python. Now there are three versions: "Numeric": The original, now at version 24.2 This is probably the last version that will be produced. "numarray": This was designed to be the "next generation" array package. It has some nice additional features that Numeric does not have, but is missing some as well. It is at version 1.5.1. it may see some bug fix releases in the future, but probably won't see any more major development. "numpy": this is the "grand unification" array package. It is based on the Numeric code base, and is designed to have the best features of Numeric and numarray, plus some extra good stuff. It is now at version 1.0beta, with an expected release date for 1.0final sometime this fall. It is under active development, the API is pretty stable now, and it appears to have the consensus of the numerical python community as the "way of the future" I wrote all that out so that you can be clear which package you are having trouble with -- you've used both the term "Numeric" and "numpy" in your posts, and there is some confusion. If you are working on a project that does not need to be released for a few months (i.e. after numpy has reached 1.0 final), I'd use numpy, rather than Numeric or numarray. Also: on OS-X, there are far to many ways to build Python. When you report a problem, you need to define exactly which python build you are using, and this goes beyond python version -- fink? darwinports? built-it-from-source? Framework? Universal, etc... The MacPython community is doing it's best to standardize on the Universal Build of 2.4.3 that you can find here: http://www.pythonmac.org/packages/py24-fat/ There you will also find pre-built packages for Numeric24.2, numarray1.5.1, and numpy0.9.8 Have you tried any of those? They should be built against Apple's vectLib. There isn't a package for numpy 1.0beta there yet. I may add one soon. > Interestingly, I can get Numeric version 23.8 to build and > run just fine, but it appears that the dotblas (BLAS > optimized matrixmultiply/dot/innerproduct) does not properly > get built in. Thus, all my matrix operations are -very- slow. I'm not sure of the dates, but that is probably a version that didn't have the check for Apple's vecLib in the setup.py, so it built with the built-in lapack-lite instead. You can compare the setup.py files from that and newer versions to see how to make it build against vectLib, but I suspect if you do that, you'll see the same problems. Also, please send a small test script that crashes for you, so others can test it. -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/OR&R/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chr...@no... |
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From: Daran L. R. <dr...@uc...> - 2006-08-10 14:58:53
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Hi Hanno, > I had a similar behaviour when I tried to use module compield with an > older f2py with a newer version of numpy. So is it maybe possible that > some *.so files are used from an earlier build? Many thanks for the reply. This was my first attempt to build and use numpy; I have no previous version. May I ask how you specificlly solved the problem on your machine? Thanks, Daran -- |
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From: Hanno K. <kl...@ph...> - 2006-08-10 14:13:01
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Daran, I had a similar behaviour when I tried to use module compield with an older f2py with a newer version of numpy. So is it maybe possible that some *.so files are used from an earlier build? Hanno "Daran L. Rife" <dr...@uc...> said: > Hi group, > > Sorry, but there was an error on my previous message, > 2nd paragraph, 2nd setence. It should read: > > Unfortunately, I am experiencing a problem that I cannot sort > out. I am running Python 2.4.3 on a Mac G5 running OS X 10.4 > Tiger (8.7.0), using gcc version 4.0.1, and the Apple > vecLib.framework which has an optimized BLAS and LAPACK. > When building Numeric 24.0, 24.1, or 24.2 everything seems > to go AOK. But when I run code which makes use of the Numeric > package (maksed arrays, dot product, LinearAlgebra, etc.) my > code crashes hard and unpredictably. When it crashes I simply > get a "Segmentation Fault". I'm sorry that I can't be more > specific about what seems to happen just before the crash... > I've tried to trace it but to no avail. > > Thanks again for your help. > > > Daran > > -- > > > I recently switched from a Debian Linux box to a Mac G5 > > PowerPC, running Mac OS X 10.4 Tiger (8.7.0). I use the > > Python Numeric package extensively, and have come to > > depend upon it. In my view, this piece of software is > > truly first rate, and it has greatly improved my > > productivity in the area of scientific analysis. > > > > Unfortunately, I am experiencing a problem that I cannot sort > > out. I am running Python 2.4.3 on a Debian box (V3.1), using > > gcc version 4.0.1, and the Apple vecLib.framework which has > > an optimized BLAS and LAPACK. When building Numeric 24.0, > > 24.1, or 24.2 everything seems to go AOK. But when I run > > code which makes use of the Numeric package (maksed arrays, > > dot product, LinearAlgebra, etc.) my code crashes hard and > > unpredictably. When it crashes I simply get a "Segmentation > > Fault". I'm sorry that I can't be more specific about what > > seems to happen just before the crash...I've tried to trace > > it but to no avail. > > > > Interestingly, I can get Numeric version 23.8 to build and > > run just fine, but it appears that the dotblas (BLAS > > optimized matrixmultiply/dot/innerproduct) does not properly > > get built in. Thus, all my matrix operations are -very- slow. > > > > Has anyone seen this problem, or know where I might look > > to solve it? Perhaps I have overlooked a crucial step in > > the build/install of Numeric 24.x on the Mac. > > > > I searched round the Net with google, and have sifted through > > the numpy/scipy/numeric Web pages, various mailing lists, user > > groups, etc., and can't seem to find any relevant info. > > > > Alternatively, can someone explain how to get Numeric 23.8 > > to compile on OS X 10.4 Tiger, with the dotblas module? > > > > > > Thanks very much for your help, > > > > > > Daran > > > > > > ------------------------------------------------------------------------- > > Using Tomcat but need to do more? Need to support web services, security? > > Get stuff done quickly with pre-integrated technology to make your job > > easier > > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo > > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 > > _______________________________________________ > > Numpy-discussion mailing list > > Num...@li... > > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > > > > > > ------------------------------------------------------------------------- > Using Tomcat but need to do more? Need to support web services, security? > Get stuff done quickly with pre-integrated technology to make your job easier > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 > _______________________________________________ > Numpy-discussion mailing list > Num...@li... > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > -- Hanno Klemm kl...@ph... |
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From: Daran L. R. <dr...@uc...> - 2006-08-10 14:02:32
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Hi group, Sorry, but there was an error on my previous message, 2nd paragraph, 2nd setence. It should read: Unfortunately, I am experiencing a problem that I cannot sort out. I am running Python 2.4.3 on a Mac G5 running OS X 10.4 Tiger (8.7.0), using gcc version 4.0.1, and the Apple vecLib.framework which has an optimized BLAS and LAPACK. When building Numeric 24.0, 24.1, or 24.2 everything seems to go AOK. But when I run code which makes use of the Numeric package (maksed arrays, dot product, LinearAlgebra, etc.) my code crashes hard and unpredictably. When it crashes I simply get a "Segmentation Fault". I'm sorry that I can't be more specific about what seems to happen just before the crash... I've tried to trace it but to no avail. Thanks again for your help. Daran -- > I recently switched from a Debian Linux box to a Mac G5 > PowerPC, running Mac OS X 10.4 Tiger (8.7.0). I use the > Python Numeric package extensively, and have come to > depend upon it. In my view, this piece of software is > truly first rate, and it has greatly improved my > productivity in the area of scientific analysis. > > Unfortunately, I am experiencing a problem that I cannot sort > out. I am running Python 2.4.3 on a Debian box (V3.1), using > gcc version 4.0.1, and the Apple vecLib.framework which has > an optimized BLAS and LAPACK. When building Numeric 24.0, > 24.1, or 24.2 everything seems to go AOK. But when I run > code which makes use of the Numeric package (maksed arrays, > dot product, LinearAlgebra, etc.) my code crashes hard and > unpredictably. When it crashes I simply get a "Segmentation > Fault". I'm sorry that I can't be more specific about what > seems to happen just before the crash...I've tried to trace > it but to no avail. > > Interestingly, I can get Numeric version 23.8 to build and > run just fine, but it appears that the dotblas (BLAS > optimized matrixmultiply/dot/innerproduct) does not properly > get built in. Thus, all my matrix operations are -very- slow. > > Has anyone seen this problem, or know where I might look > to solve it? Perhaps I have overlooked a crucial step in > the build/install of Numeric 24.x on the Mac. > > I searched round the Net with google, and have sifted through > the numpy/scipy/numeric Web pages, various mailing lists, user > groups, etc., and can't seem to find any relevant info. > > Alternatively, can someone explain how to get Numeric 23.8 > to compile on OS X 10.4 Tiger, with the dotblas module? > > > Thanks very much for your help, > > > Daran > > > ------------------------------------------------------------------------- > Using Tomcat but need to do more? Need to support web services, security? > Get stuff done quickly with pre-integrated technology to make your job > easier > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 > _______________________________________________ > Numpy-discussion mailing list > Num...@li... > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > |
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From: Daran L. R. <dr...@uc...> - 2006-08-10 13:33:56
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Hello,
I am a veteran user of Numeric and am trying
out the latest version of numpy (numpy 1.01b)
on Mac OS X 10.4 Tiger (8.7.0).
When trying to invert a matrix with
numpy.linalg.inv I get the following error:
---->
Traceback (most recent call last):
File "./bias_correction.py", line 381, in ?
if __name__ == "__main__": main()
File "./bias_correction.py", line 373, in main
(index_to_stnid, bias_and_innov) = calc_bias_and_innov(cf, stn_info,
obs, infile_obs, grids, infile_grids)
File "./bias_correction.py", line 297, in calc_bias_and_innov
K = make_kalman_gain(R, P_local, H)
File "./bias_correction.py", line 157, in make_kalman_gain
K = MA.dot( MA.dot(P, MA.transpose(H)), inv(MA.dot(H, MA.dot(P,
MA.transpose(H))) + R ) )
File "/opt/python/lib/python2.4/site-packages/numpy/linalg/linalg.py",
line 149, in inv
return wrap(solve(a, identity(a.shape[0], dtype=a.dtype)))
TypeError: __array_wrap__() takes exactly 3 arguments (2 given)
<----
Is this a known problem, and if so, what is the fix?
Thanks very much,
Daran
|
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From: Travis O. <oli...@ie...> - 2006-08-10 12:55:51
|
Pierre Barbier de Reuille wrote: > Hi, > > in my documentation, the copyswap function in the PyArray_ArrFuncs > structure is supposed to have this signature: > > copyswap (void) (void* dest, void* src, int swap, int itemsize) > > However, in the latest version of NumPy, the signature is: > > copyswap (void) (void*, void*, int, void*) > > My question is: what correspond to the last void* ? > It's only needed for FLEXIBLE arrays (STRING, UNICODE, VOID), then you pass in an array whose ->descr member has the right itemsize. Look in core/src/arratypes for the definitions of the copyswap functions which can be helpful to see if arguments are actually needed. -Travis |
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From: Pierre B. de R. <pb...@cm...> - 2006-08-10 11:38:57
|
Hi, in my documentation, the copyswap function in the PyArray_ArrFuncs structure is supposed to have this signature: copyswap (void) (void* dest, void* src, int swap, int itemsize) However, in the latest version of NumPy, the signature is: copyswap (void) (void*, void*, int, void*) My question is: what correspond to the last void* ? Thanks, Pierre |
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From: Daran L. R. <dr...@uc...> - 2006-08-10 05:45:54
|
Hello, I recently switched from a Debian Linux box to a Mac G5 PowerPC, running Mac OS X 10.4 Tiger (8.7.0). I use the Python Numeric package extensively, and have come to depend upon it. In my view, this piece of software is truly first rate, and it has greatly improved my productivity in the area of scientific analysis. Unfortunately, I am experiencing a problem that I cannot sort out. I am running Python 2.4.3 on a Debian box (V3.1), using gcc version 4.0.1, and the Apple vecLib.framework which has an optimized BLAS and LAPACK. When building Numeric 24.0, 24.1, or 24.2 everything seems to go AOK. But when I run code which makes use of the Numeric package (maksed arrays, dot product, LinearAlgebra, etc.) my code crashes hard and unpredictably. When it crashes I simply get a "Segmentation Fault". I'm sorry that I can't be more specific about what seems to happen just before the crash...I've tried to trace it but to no avail. Interestingly, I can get Numeric version 23.8 to build and run just fine, but it appears that the dotblas (BLAS optimized matrixmultiply/dot/innerproduct) does not properly get built in. Thus, all my matrix operations are -very- slow. Has anyone seen this problem, or know where I might look to solve it? Perhaps I have overlooked a crucial step in the build/install of Numeric 24.x on the Mac. I searched round the Net with google, and have sifted through the numpy/scipy/numeric Web pages, various mailing lists, user groups, etc., and can't seem to find any relevant info. Alternatively, can someone explain how to get Numeric 23.8 to compile on OS X 10.4 Tiger, with the dotblas module? Thanks very much for your help, Daran |
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From: Paul D. <pfd...@gm...> - 2006-08-10 04:55:42
|
P. F. Dubois, K. Hinsen, and J. Hugunin, "Numerical Python", Computers in Physics, v. 10, #3, May/June 1996. is one reference people have used. Others simply refer to the website. The new book might be the best for numpy itself, dunno. Related papers are: David Ascher, P. F. Dubois, Konrad Hinsen, James Hugunin, and Travis Oliphant, "Numerical Python", UCRL-MA-128569, 93 pp., Lawrence Livermore National Laboratory, Livermore, CA; 1999. -- this is the 'official' Numerical Python documentation as first released. P. F. Dubois, "Extending Python with Fortran", Computing in Science and Engineering, v. 1 #5, Sept./Oct. 1999., p.66-73. David Scherer, Paul Dubois, and Bruce Sherwood, "VPython: 3D Interactive Scientific Graphics for Students", Computing in Science and Engineering, v. 2 #5, Sep./Oct. 2000, p. 56-62. On 09 Aug 2006 21:37:39 -0700, Sebastian Haase <ha...@ms...> wrote: > Hi, > we are using numerical python as an integral part of a microscope > development project over last few years. > > So far we have been using exclusively numarray but now I decided it's > time to slowly but sure migrate to numpy. > > What is the proper way to reference these packages ? > > Thanks to everyone involved, > Sebastian Haase > UCSF > > > ------------------------------------------------------------------------- > Using Tomcat but need to do more? Need to support web services, security? > Get stuff done quickly with pre-integrated technology to make your job easier > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 > _______________________________________________ > Numpy-discussion mailing list > Num...@li... > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > > > |
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From: Sebastian H. <ha...@ms...> - 2006-08-10 04:36:53
|
Hi, we are using numerical python as an integral part of a microscope development project over last few years. So far we have been using exclusively numarray but now I decided it's time to slowly but sure migrate to numpy. What is the proper way to reference these packages ? Thanks to everyone involved, Sebastian Haase UCSF |
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From: Sebastian H. <ha...@ms...> - 2006-08-10 04:35:36
|
Travis Oliphant wrote:
> Sebastian Haase wrote:
>> On Wednesday 09 August 2006 15:45, you wrote:
>>
>>> Sebastian Haase wrote:
>>>
>>>> On Wednesday 09 August 2006 15:18, Travis Oliphant wrote:
>>>>
>>>>> If numarray supported it, then we should get NumPy to support it as
>>>>> well
>>>>> unless there is a compelling reason not to. I can't think of any
>>>>> except
>>>>> that it might be hard to make it work. What is '0i4' supposed to mean
>>>>> exactly? Do you get a zero-sized field or is the field not included?
>>>>> I think the former will be much easier than the latter. Would
>>>>> that be
>>>>> O.K.?
>>>>>
>>>> That's exactly what numarray did. The rest of my code is assuming that
>>>> all fields exist (even if they are empty). Otherwise I get a name
>>>> error which is worse than getting an empty array.
>>>>
>>> Do you have a simple code snippet that I could use as a test?
>>>
>>> -Travis
>>>
>>
>> I think this should do it:
>>
>> a = N.arange(10, dtype=N.float32)
>> a.shape = 5,2
>> type_descr = [("int", "<0i4"),("float", "<2f4")]
>> a.dtype = type_descr
>>
>>
>
> I'm not sure what a.shape = (5,2) is supposed to do. I left it in the
> unit-test out because assigning to the data-type you just defined
> already results in
>
> a['float'].shape being (5,2)
>
> If it is left in, then an extra dimension is pushed in and
>
> a['float'].shape is (5,1,2)
>
>
> This is due to the default behavior of assigning data-types when the new
> data-type has a larger but compatibile itemsize then the old itemsize.
I have to admit that I don't understand that statement. I thought - just
"visually" - that a.shape = 5,2 would make a "table" with 2 columns.
Then I could go on and give those columns names...
Or is the problem that the type "2f4" refers to (some sort of) a "single
column" with 2 floats grouped together !?
Thanks for implementing it so quickly,
Sebastian Haase
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