Mailman 3 May 2006 - NumPy-Discussion

[fwd] here's a winer
by mzcdobk hxuqvubp May 29, 2006

May 29, 2006

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dtype: hashing and cmp
by Simon Burton May 28, 2006

May 28, 2006

Is there a reason why dtype's are unhashable ? (ouch) On another point, is there a canonical list of dtype's ? I'd like to test the dtype of an array, and I always end up with something like this: if array.dtype == numpy.dtype('l'): ... When I would prefer to write something like: if array.dtype == numpy.Int32: ... (i can never remember these char codes !) Alternatively, should dtype's __cmp__ promote the other arg to a dtype before the compare ? I guess not, since that would break a lot of code: eg. dtype(None) is legal. Simon. -- Simon Burton, B.Sc. Licensed PO Box 8066 ANU Canberra 2601 Australia Ph. 61 02 6249 6940 http://arrowtheory.com

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fromfile for dtype=Int8
by Erin Sheldon May 28, 2006

May 28, 2006

Hi everyone - The "fromfile" method isn't working for Int8 in ascii mode: # cat test.dat 3 4 5 >>> import numpy as np >>> np.__version__ '0.9.9.2547' >>> np.fromfile('test.dat', sep='\n', dtype=np.Int16) array([3, 4, 5], dtype=int16) >>> np.fromfile('test.dat', sep='\n', dtype=np.Int8) Traceback (most recent call last): File "<stdin>", line 1, in ? ValueError: don't know how to read character files with that array type Was this intended? Erin

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shea 7160
by Hughie Bylsma May 26, 2006

May 26, 2006

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Re:oil trucks
by mandy May 25, 2006

May 25, 2006

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numpy.sin
by Alan G Isaac May 25, 2006

May 25, 2006

I am a user, not a numerics type, so this is undoubtedly a naive question. Might the sin function be written to give greater accuracy for large real numbers? It seems that significant digits are in some sense being discarded needlessly. I.e., compare these: >>> sin(linspace(0,10*pi,11)) array([ 0.00000000e+00, 1.22460635e-16, -2.44921271e-16, 3.67381906e-16, -4.89842542e-16, 6.12303177e-16, -7.34763812e-16, 8.57224448e-16, -9.79685083e-16, 1.10214572e-15, -1.22460635e-15]) >>> sin(linspace(0,10*pi,11)%(2*pi)) array([ 0.00000000e+00, 1.22460635e-16, 0.00000000e+00, 1.22460635e-16, 0.00000000e+00, 1.22460635e-16, 0.00000000e+00, 1.22460635e-16, 0.00000000e+00, 1.22460635e-16, 0.00000000e+00]) Just wondering. Cheers, Alan Isaac

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Problem compiling numpy against ATLAS on amd64 (Mandriva 2006)
by Yi Qiang May 25, 2006

May 25, 2006

Hi list, I searched the archives and found various threads regarding this issue and I have not found a solution there. software versions: gfortran 4.0.1 atlas 3.6.0 lapack 3.0 Basically numpy spits out this message when I try to compile it: gcc: numpy/linalg/lapack_litemodule.c /usr/bin/gfortran -shared build/temp.linux-x86_64-2.4/numpy/linalg/lapack_litemodule.o -L/usr/local/lib/atlas -llapack -lptf77blas -lptcblas -latlas -lgfortran -o build/lib.linux-x86_64-2.4/numpy/linalg/lapack_lite.so /usr/bin/ld: /usr/local/lib/atlas/liblapack.a(dlamch.o): relocation R_X86_64_32S against `a local symbol' can not be used when making a shared object; recompile with -fPIC /usr/local/lib/atlas/liblapack.a: could not read symbols: Bad value collect2: ld returned 1 exit status /usr/bin/ld: /usr/local/lib/atlas/liblapack.a(dlamch.o): relocation R_X86_64_32S against `a local symbol' can not be used when making a shared object; recompile with -fPIC /usr/local/lib/atlas/liblapack.a: could not read symbols: Bad value collect2: ld returned 1 exit status error: Command "/usr/bin/gfortran -shared build/temp.linux-x86_64-2.4/numpy/linalg/lapack_litemodule.o -L/usr/local/lib/atlas -llapack -lptf77blas -lptcblas -latlas -lgfortran -o build/lib.linux-x86_64-2.4/numpy/linalg/lapack_lite.so" failed with exit status 1 However I have compiled all the software explicitly with the -fPIC flag on. Attached is my make.inc for LAPACK and my Makefile for ATLAS. I followed these instructions to create a hybrid LAPACK/ATLAS archive: http://math-atlas.sourceforge.net/errata.html#completelp Interestingly enough, if I just use the bare version of ATLAS, numpy compiles fine. If I use the bare version of LAPACK, numpy compiles fine . Any help would be greatly appreciated. -Yi

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Numpy 0.9.8 fails under Mac OS X
by Rudolph van der Merwe May 24, 2006

May 24, 2006

I built and installed Numpy 0.9.8 successfully from the source using python setup.py build sudo python setup.py install When I run the unit-test though, it fails with the following error: [~] python 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.__version__ '0.9.8' >>> numpy.test(1,1) Found 5 tests for numpy.distutils.misc_util Found 3 tests for numpy.lib.getlimits Found 30 tests for numpy.core.numerictypes Found 13 tests for numpy.core.umath Found 1 tests for numpy.core.scalarmath Found 8 tests for numpy.lib.arraysetops Found 42 tests for numpy.lib.type_check Found 95 tests for numpy.core.multiarray Found 3 tests for numpy.dft.helper Found 36 tests for numpy.core.ma Found 9 tests for numpy.lib.twodim_base Found 2 tests for numpy.core.oldnumeric Found 9 tests for numpy.core.defmatrix Found 1 tests for numpy.lib.ufunclike Found 35 tests for numpy.lib.function_base Found 1 tests for numpy.lib.polynomial Found 6 tests for numpy.core.records Found 19 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__ ...................................................python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd668; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd4e8; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd650; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd560; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd4a0; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd3f8; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd470; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd320; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd728; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd440; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd410; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd590; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug python(9463) malloc: *** Deallocation of a pointer not malloced: 0xd458; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug F............................................................................................................................................................................................................................................................................................................................ ====================================================================== 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 63, in check_types assert val.dtype.num == typeconv[k,l] and \ AssertionError: error with (13,14) ---------------------------------------------------------------------- Ran 368 tests in 1.517s FAILED (failures=1) <unittest.TextTestRunner object at 0x1052910> Any idea what is causing this? Thanks Rudolph -- Rudolph van der Merwe

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need help building on windows
by Christopher Hanley May 24, 2006

May 24, 2006

Hi, I am new to working on the Windows XP OS and I am having a problem building numpy from source. I am attempting to build using the MinGW compilers on a Windows XP box which has Python 2.4.1 installed. I do not have nor do I intend to use any optimized libraries like ATLAS. I have checked out numpy revision 2542. This is as clean a build as you could probably get. I am attaching my build log to this message. If anyone has any helpful hints they would be most appreciated. Thanks, Chris

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Dot product threading?
by Angus McMorland May 24, 2006

May 24, 2006

Is there a way to specify which dimensions I want dot to work over? For example, if I have two arrays: In [78]:ma = array([4,5,6]) # shape = (1,3) In [79]:mb = ma.transpose() # shape = (3,1) In [80]:dot(mb,ma) Out[80]: array([[16, 20, 24], [20, 25, 30], [24, 30, 36]]) No problems there. Now I want to do that multiple times, threading over the first dimension of larger arrays: In [85]:mc = array([[[4,5,6]],[[7,8,9]]]) # shape = (2, 1, 3) In [86]:md = array([[[4],[5],[6]],[[7],[8],[9]]]) #shape = (2, 3, 1) and I want to calculate the two (3, 1) x (1, 3) dot products to get a shape = (2, 3, 3) result, so that res[i,...] == dot(md[i,...], mc[i,...]) >From my example above res[0,...] would be the same as dot(mb,ma) and res[1,...] would be In [108]:dot(md[1,...],mc[1,...]) Out[108]: array([[49, 56, 63], [56, 64, 72], [63, 72, 81]]) I could do it by explicitly looping over the first dimension (as suggested by my generic example), but it seems like there should be a better way by specifying to the dot function the dimensions over which it should be 'dotting'. Cheers, Angus. -- Angus McMorland email a.mcmorland(a)auckland.ac.nz mobile +64-21-155-4906 PhD Student, Neurophysiology / Multiphoton & Confocal Imaging Physiology, University of Auckland phone +64-9-3737-599 x89707 Armourer, Auckland University Fencing Secretary, Fencing North Inc.

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