Re: [Numpy-discussion] ***[Possible UCE]*** Bug in memmap/python allocation code?
Mike Ressler wrote: I'm trying to work with memmaps on very large files, i.e. 2 GB, up to 10 GB. The files are data cubes of images (my largest is 1290(x)x1024(y)x2011(z)) and my immediate task is to strip the data from 32-bits down to 16, and to rearrange some of the data on a per-xy-plane basis. I'm running this on a Fedora Core 5 64-bit system, with python-2.5b2 (that I believe I compiled in 64-bit mode) and numpy-1.0b1. The disk has 324 GB free space. I just discovered the problem. All the places where PyObject_AsRead/WriteBuffer is used needs to have the final argument changed to Py_ssize_t (which in arrayobject.h is defined as int if you are using less than Python 2.5). This should be fixed in SVN shortly -Travis - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] trivial question: how to compare dtype - but ignoring byteorder ?
On Tuesday 25 July 2006 01:42, Bill Baxter wrote:
And I think byteorder matters when comparing dtypes:
numpy.dtype('f4') == numpy.dtype('f4')
False
Oh -- that '' part is indicating *byte order* ?!
I thought it was odd that numpy could only tell me the type was less
than f4, which I assumed must be shorthand for less than or equal to
f4. Makes much more sense now!
--bb
Yep! And there are then four possiblities.
'' - big-endian
'' - little
'|' - not-applicable
'=' - native
Karol
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Re: [Numpy-discussion] Bug in memmap/python allocation code?
On Tuesday 25 July 2006 02:36, Mike Ressler wrote:
I'm trying to work with memmaps on very large files, i.e. 2 GB, up to 10
GB. The files are data cubes of images (my largest is
1290(x)x1024(y)x2011(z)) and my immediate task is to strip the data from
32-bits down to 16, and to rearrange some of the data on a per-xy-plane
basis. I'm running this on a Fedora Core 5 64-bit system, with
python-2.5b2(that I believe I compiled in 64-bit mode) and
numpy-1.0b1. The disk has 324 GB free space.
The log from a minimal case is as follows:
ressler python2.5
Python 2.5b2 (r25b2:50512, Jul 18 2006, 12:58:29)
[GCC 4.1.1 20060525 (Red Hat 4.1.1-1)] on linux2
Type help, copyright, credits or license for more information.
import numpy as np
data=np.memmap('temp_file',mode='w+',shape=(2011,1280,1032),dtype='h')
size = 2656450560
bytes = 5312901120
len(mm) = 5312901120
(2011, 1280, 1032) h 0 0
Traceback (most recent call last):
File stdin, line 1, in module
File /usr/local/lib/python2.5/site-packages/numpy/core/memmap.py, line
75, in __new__
offset=offset, order=order)
TypeError: buffer is too small for requested array
If I have a small number of frames (z=800 rather than 2011), this all works
fine. I've added a few lines to memmap.py to print some diagnostic
information - the error occurs on line 71 in the original memmap.py file,
not 75. The size = and bytes = lines show that memmap.py is calculating
the correct size for the buffer, and the len(mm) shows that the python
mmap.mmap call on line 67 is returning a buffer of the correct size. The
(2011, 1280, 1032) h 0 0 bit is from a print statement that was left in
the source file by the authors, and indicates what the following self =
ndarray.__new__ call is trying to do. However, it is the ndarray.__new__
call that is breaking down, and I don't really have enough skill to
continue chasing it down. I took a quick look at the C source, but I
couldn't figure out where the ndarray.__new__ is actually defined.
Any suggestions to help me past this? Thanks.
Mike
I know Travis has nswered in a different thread. Let me jsut add where the
actual error is raised - maybe it will be of some use. It is around line 5490
of arrayobject.c (procedure array_new):
else { /* buffer given -- use it */
if (dims.len == 1 dims.ptr[0] == -1) {
dims.ptr[0] = (buffer.len-(intp)offset) / itemsize;
}
else if ((strides.ptr == NULL) \
buffer.len itemsize* \
PyArray_MultiplyList(dims.ptr, dims.len)) {
PyErr_SetString(PyExc_TypeError,
buffer is too small for \
requested array);
goto fail;
}
So it does look like an overflow to me.
Karol
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Re: [Numpy-discussion] (no subject)
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Re: [Numpy-discussion] how to get an array with varying poisson distribution
Robert Kern wrote: Sebastian Haase wrote: Hi, Essentially I'm looking for the equivalent of what was in numarray: from numarray import random_array random_array.poisson(arr) That is: if for example arr is a 256x256 array of positive integers, then this returns a new array of random numbers than are drawn according to the poisson statistics where arr's value at coordinate y,x determines the mean of the poisson distribution used to generate a new value for y,x. I'm afraid that at this point in time, the distributions only accept scalar values for the parameters. I've thought about reimplementing the distribution functions as ufuncs, but that's a hefty chunk of work that won't happen for 1.0. FWIW, I've had enquires about the availability, or not, of this functionality in NumPy as well, so when someone does have time to work on it, it will be very much appreciated. -- You see stars that clear have been dead for years But the idea just lives on... -- Bright Eyes - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] ctypes, numpy-array
Hey Lars -Original Message- From: [EMAIL PROTECTED] [mailto:numpy- [EMAIL PROTECTED] On Behalf Of Lars Friedrich Sent: 25 July 2006 12:50 To: numpy-discussion@lists.sourceforge.net Subject: [Numpy-discussion] ctypes, numpy-array Hello, I would like to work with some data using python/numpy. The data is generated with C. To bring the data from C to python, I would like to use ctype. I am using python 2.4.3 on a windows-System. To accomplish the described task, I have the following plan. Please tell me, if this is possible, respectively if there is a better way. 1) In C, I write a .dll, that has a function int foo(PyObject *arg) 2) In python, I generate a numpy-array with the appropriate size. e.g. a = zeros((3,3), dtype=int) 3) From python, I call my .dll-function with a as an argument: windll.mydll.foo(a) What's might be happening here is that a.ctypes.data is in fact being passed to your function via ctypes's from_param magic (check the ctypes tutorial for details). In [10]: x = N.array([]) In [11]: x.ctypes.data Out[11]: c_void_p(15502816) In [12]: x._as_parameter_ Out[12]: 15502816 4) In the foo-function in the C-.dll I cast the pointer and access the data-field. PyArrayObject *myPtr = (PyArrayObject*) arg; myPtr-data[0] = 1; return 0; However, when I do this, I get an AccessViolationError writing 0x0 So what's probably happening here is that you already have a pointer to the array's data which you then cast to a PyArrayObject pointer. Dereferencing myPtr-data is looking for a pointer inside the array's data, which contains zeros. Here's a few things to try: - look at ctypes's PyDLL option if you want to pass around Python objects - Write your function as: int foo(int* x); Then do something like this: x = N.array([...], dtype=N.intc) mydll.foo.restype = None mydll.foo.argtypes = [POINTER(c_int)] mydll.foo(x.ctypes.data) This might also work: x = N.array([...], dtype=N.intc) mydll.foo.restype = None mydll.foo(x) Cheers, Albert - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] ctypes, numpy-array
Hello all -Original Message- From: [EMAIL PROTECTED] [mailto:numpy- [EMAIL PROTECTED] On Behalf Of Lars Friedrich Sent: 25 July 2006 13:55 To: numpy-discussion@lists.sourceforge.net Subject: Re: [Numpy-discussion] ctypes, numpy-array What's might be happening here is that a.ctypes.data is in fact being passed to your function via ctypes's from_param magic (check the ctypes tutorial for details). In [10]: x = N.array([]) In [11]: x.ctypes.data Out[11]: c_void_p(15502816) In [12]: x._as_parameter_ Out[12]: 15502816 OK, I did not know about x.ctypes.data... Travis added this quite recently. Somebody (probably me) still has to update the wiki to reflect these changes. So what's probably happening here is that you already have a pointer to the array's data which you then cast to a PyArrayObject pointer. Dereferencing myPtr-data is looking for a pointer inside the array's data, which contains zeros. I understand. - look at ctypes's PyDLL option if you want to pass around Python objects ??? You can read about PyDLL here: http://docs.python.org/dev/lib/ctypes-loading-shared-libraries.html I think PyDLL might turn out to be an interesting alternative to traditional extension modules. But for wrapping C code, I think writing functions that operate on pointers to ints and floats and whatnot works nicely. - Write your function as: int foo(int* x); Then do something like this: x = N.array([...], dtype=N.intc) mydll.foo.restype = None Slight typo on my part. For this example it should be: mydll.foo.restype = c_int mydll.foo.argtypes = [POINTER(c_int)] mydll.foo(x.ctypes.data) I did that, and it worked fine for me. Thank you very much! This is really great. Cool. Enjoy! Regards, Albert - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Problem converting a numarray extension to numpy
On 7/24/06, Travis Oliphant [EMAIL PROTECTED] wrote: Paul Barrett wrote: I'm having a problem converting a C extension module that was originally written for numarray to use numpy. I using swig to create a wrapper flle for the C code. I have added the numpy.get_numarray_include() method to my setup.py file and have changed the numarray/libnumarray.h to use numpy/libnumarray.h. The extension appears to compile fine (with the exception of some warning messages). However, when I import the module, I get a segfault. Do I need to add anything else to the share library's initialization step other than import_libnumarray()? No, that should be enough. The numarray C-API has only been tested on a few extension modules. It's very possible some of the calls have problems. It's also possible you have an older version of numpy lying around somewhere. Do you get any kind of error message on import? No. I'm using a recent SVN version of numpy and I remove the install and build directories before every new build, i.e. I do clean build after each SVN update. No. Just the segfault. I guess the best thing to do is put in print statements and try to locate where it fails. Thanks for the clarification, Travis. -- Paul - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] I've created a 1.0b1 release tag in SVN
I just built numpy-1.0b1 on Mac OS X (10.4.7) using gcc 4.0 and it fails 2 of the regression tests. Here is the output: === ActivePython 2.4.3 Build 11 (ActiveState Software Inc.) based on Python 2.4.3 (#1, Apr 3 2006, 18:07:18) [GCC 3.3 20030304 (Apple Computer, Inc. build 1666)] on darwin Type help, copyright, credits or license for more information. import numpy numpy.test() Found 5 tests for numpy.distutils.misc_util Found 3 tests for numpy.lib.getlimits Found 30 tests for numpy.core.numerictypes Found 32 tests for numpy.linalg Found 13 tests for numpy.core.umath Found 4 tests for numpy.core.scalarmath Found 8 tests for numpy.lib.arraysetops Found 42 tests for numpy.lib.type_check Found 147 tests for numpy.core.multiarray Found 3 tests for numpy.dft.helper Found 36 tests for numpy.core.ma Found 10 tests for numpy.lib.twodim_base Found 10 tests for numpy.core.defmatrix Found 1 tests for numpy.lib.ufunclike Found 39 tests for numpy.lib.function_base Found 1 tests for numpy.lib.polynomial Found 8 tests for numpy.core.records Found 26 tests for numpy.core.numeric Found 4 tests for numpy.lib.index_tricks Found 46 tests for numpy.lib.shape_base Found 0 tests for __main__ ...F..F. == FAIL: check_large_types (numpy.core.tests.test_scalarmath.test_power) -- Traceback (most recent call last): File /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/numpy/core/tests/test_scalarmath.py, line 47, in check_large_types assert b == 6765201, error with %r: got %r % (t,b) AssertionError: error with type 'float128scalar': got 0.0 == FAIL: check_types (numpy.core.tests.test_scalarmath.test_types) -- Traceback (most recent call last): File /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/numpy/core/tests/test_scalarmath.py, line 20, in check_types assert a == 1, error with %r: got %r % (atype,a) AssertionError: error with type 'float128scalar': got 0.0 -- Ran 468 tests in 1.658s FAILED (failures=2) unittest.TextTestRunner object at 0x10bab90 === On 7/25/06, Damien Miller [EMAIL PROTECTED] wrote: On Fri, 21 Jul 2006, Travis Oliphant wrote: I've created the 1.0b1 release tag in SVN and will be uploading files shortly to Sourceforge. FYI numpy-1.0b1 builds fine and passes all its regression tests on OpenBSD -current. -d - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion -- Rudolph van der Merwe - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] ctypes, numpy-array
Lars Friedrich wrote: I would like to work with some data using python/numpy. The data is generated with C. To bring the data from C to python, I would like to use ctype. I am using python 2.4.3 on a windows-System. To accomplish the described task, I have the following plan. Please tell me, if this is possible, respectively if there is a better way. 1) In C, I write a .dll, that has a function int foo(PyObject *arg) I'm a bit confused here. I thought the primary point of ctypes was to be able to access existing, non-python-aware dlls from Python without writing C code. In this case, you're writing a dll that understands PyObjects (or, I assume, a particular PyObject -- a numarray). Why not just forget ctypes and write a regular old extension? Or use Pyrex, or Boost, or. Maybe ctypes has some real advantages I don't get. -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/ORR/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception [EMAIL PROTECTED] - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] fixing diag() for matrices
Hi, there was a thread about this before, diag() is currently only partly useful if you work with numpy-matrices, because the 1d-2d direction doesn't work, as there are no 1d-numpy-matrices. This is unfortunate because a numpy-matrix with shape (n,1) or (1,m) should be naturally treated as a vector, imho. So it would be nice if this could be fixed. It's probably not the most efficient solution, but what I want for numpy-matrix input x is to get: mat(diag(x.A.squeeze)) where diag is the current implementation. This means that if x is not a vector (truly 2d), then nothing is changed. But if one of the dimensions of x is ==1, then it's turned into a 1d-array, and diag works as it should. Does that sound reasonable? Thanks, Sven - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] fixing diag() for matrices
Robert Kern schrieb: Sven Schreiber wrote: Hi, there was a thread about this before, diag() is currently only partly useful if you work with numpy-matrices, because the 1d-2d direction doesn't work, as there are no 1d-numpy-matrices. This is unfortunate because a numpy-matrix with shape (n,1) or (1,m) should be naturally treated as a vector, imho. So it would be nice if this could be fixed. It's probably not the most efficient solution, but what I want for numpy-matrix input x is to get: mat(diag(x.A.squeeze)) where diag is the current implementation. This means that if x is not a vector (truly 2d), then nothing is changed. But if one of the dimensions of x is ==1, then it's turned into a 1d-array, and diag works as it should. Does that sound reasonable? Not for numpy.diag() in my opinion. However, I won't object to a numpy.matlib.diag() that knows about matrix objects and behaves the way you want. That would be fine with me. However, I'd like to point out that after some bug-squashing currently all numpy functions deal with numpy-matrices correctly, afaik. The current behavior of numpy.diag could be viewed as a violation of that principle. (Because if x has shape (n,1), diag(x) returns only the first entry, which is pretty stupid for a diag-function operating on a vector.) I repeat, the matlib solution would be ok for me, but in some sense not fixing numpy.diag could contribute to the feeling of matrices being only second-class citizens. cheers, Sven - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] ctypes, numpy-array
Lars Friedrich wrote: In this case, you're writing a dll that understands PyObjects (or, I assume, a particular PyObject -- a numarray). Why not just forget ctypes and write a regular old extension? good point. I am relatively new to Python. The first thing I did was trying to write a regular extension. The problem is, that I *have to* use a windows-machine. And at the moment, only Visual C++ 6.0 is available here. The problem is that for a regular extension, Python and the extension need to be compiled by the same compiler AFAIK. That's mostly true. You have three options: 1) re-compile python yourself -- but then you'd also have to re-compile all the other extensions you use! 2) You can also use MingGW to compile extensions -- it takes a bit of kludging, but it can be done, and works fine once you've got it set up. Google will help you figure out how -- it's been a while since I've done it. I have to say that I find it ironic that you can use MinGW, but not other versions of the MS compiler! 3) MS distributes a command line version of their compiler for free that can be used. Again, google should help you find out how to do that. However, as other posters mentioned, you can use ctypes as it was intended with numpy -- that may be the way to go -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/ORR/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception [EMAIL PROTECTED] - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] Best numpy code to learn from?
Hi, I've written some numpy functions for grouping, translating and subtotalling data. At the moment they exist as pure Python code, but I have started rewriting them in C for speed. As this is my first attempt at a C extension for numpy, I'd appreciate any suggestions for good numpy coding style to make this work for strided arrays, multiple data types, etc. My first go at a C version is very low level (following bincount()'s code in _compiled_base.c as a guide) and works for integer and character arrays. Are there other (possible more recent) numpy source files that I should use as a guide to writing fast, clean, flexible code? Cheers Stephen - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Best numpy code to learn from?
On 7/25/06, Stephen Simmons [EMAIL PROTECTED] wrote: Hi,I've written some numpy functions for grouping, translating andsubtotalling data. At the moment they exist as pure Python code, but Ihave started rewriting them in C for speed. Did you check out Pyrex first, before looking into writing C extensions?Dave - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] lapack too small?
When I try and build I get the warning * Lapack library (from ATLAS) is probably incomplete: size of /u/fuego0b/myeates/ATLAS/lib/SunOS_HAMMER32SSE3/liblapack.a is 318k (expected 4000k) Follow the instructions in the KNOWN PROBLEMS section of the file numpy/INSTALL.txt. * But, there is no such file INSTALL.txt Whats wrong? This is on Solaris and I built the ATLAS libs myself. Mathew - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] lapack too small?
Hey Mathew The problem is that ATLAS doesn't provide all the LAPACK functions, only a few that the ATLAS developers have optimized. To get a complete LAPACK library, you need to build the Fortran LAPACK library, and then put the ATLAS-optimized functions into this library. Details here: http://math-atlas.sourceforge.net/errata.html#completelp Regards, Albert -Original Message- From: [EMAIL PROTECTED] [mailto:numpy- [EMAIL PROTECTED] On Behalf Of Mathew Yeates Sent: 26 July 2006 00:29 To: Numpy-discussion@lists.sourceforge.net Subject: [Numpy-discussion] lapack too small? When I try and build I get the warning * Lapack library (from ATLAS) is probably incomplete: size of /u/fuego0b/myeates/ATLAS/lib/SunOS_HAMMER32SSE3/liblapack.a is 318k (expected 4000k) Follow the instructions in the KNOWN PROBLEMS section of the file numpy/INSTALL.txt. * But, there is no such file INSTALL.txt Whats wrong? This is on Solaris and I built the ATLAS libs myself. Mathew - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
