Mailman 3 September 2006 - NumPy-Discussion

PyArray_ContiguousFromObject & PyDECREF
by Michiel Jan Laurens de Hoon 17 May '11

17 May '11

Dear pythoneers, In Chapter 12 ("Writing a C extension") in the Numerical Python manual, in the first example PyArray_ContiguousFromObject is used together with PyDECREF, while in the last example PyArray_ContiguousFromObject is used but the arrays are not PyDECREF'ed before the function returns. From Chapter 13 ("C API Reference") it seems that a PyDECREF is needed in both cases. Or am I missing something here? Is PyDECREF not needed for some reason in the last example? Thanks in advance, Michiel de Hoon U Tokyo First example: ============== static PyObject * trace(PyObject *self, PyObject *args) { PyObject *input; PyArrayObject *array; double sum; int i, n; if (!PyArg_ParseTuple(args, "O", &input)) return NULL; array = (PyArrayObject *) PyArray_ContiguousFromObject(input, PyArray_DOUBLE, 2, 2); if (array == NULL) return NULL; n = array->dimensions[0]; if (n > array->dimensions[1]) n = array->dimensions[1]; sum = 0.; for (i = 0; i < n; i++) sum += *(double *)(array->data + i*array->strides[0] + i*array->strides[1]); Py_DECREF(array); return PyFloat_FromDouble(sum); } Last example: ============= static PyObject * matrix_vector(PyObject *self, PyObject *args) { PyObject *input1, *input2; PyArrayObject *matrix, *vector, *result; int dimensions[1]; double factor[1]; double real_zero[1] = {0.}; long int_one[1] = {1}; long dim0[1], dim1[1]; extern dgemv_(char *trans, long *m, long *n, double *alpha, double *a, long *lda, double *x, long *incx, double *beta, double *Y, long *incy); if (!PyArg_ParseTuple(args, "dOO", factor, &input1, &input2)) return NULL; matrix = (PyArrayObject *) PyArray_ContiguousFromObject(input1, PyArray_DOUBLE, 2, 2); if (matrix == NULL) return NULL; vector = (PyArrayObject *) PyArray_ContiguousFromObject(input2, PyArray_DOUBLE, 1, 1); if (vector == NULL) return NULL; if (matrix->dimensions[1] != vector->dimensions[0]) { PyErr_SetString(PyExc_ValueError, "array dimensions are not compatible"); return NULL; } dimensions[0] = matrix->dimensions[0]; result = (PyArrayObject *)PyArray_FromDims(1, dimensions, PyArray_DOUBLE); if (result == NULL) return NULL; dim0[0] = (long)matrix->dimensions[0]; dim1[0] = (long)matrix->dimensions[1]; dgemv_("T", dim1, dim0, factor, (double *)matrix->data, dim1, (double *)vector->data, int_one, real_zero, (double *)result->data, int_one); return PyArray_Return(result); } -- Michiel de Hoon, Assistant Professor University of Tokyo, Institute of Medical Science Human Genome Center 4-6-1 Shirokane-dai, Minato-ku Tokyo 108-8639 Japan http://bonsai.ims.u-tokyo.ac.jp/~mdehoon

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Re: [Numpy-discussion] Floating point exception with numpy and embedded python interpreter
by Andrew Straw 20 Sep '07

20 Sep '07

Arkaitz Bitorika wrote: > > On 19 Apr 2006, at 18:37, Andrew Straw wrote: > >> >>> I've been embedding Python for ages on the same program and other >>> modules work fine, only numpy fails. >> >> >> Most other modules don't use the SSE units, so wouldn't get hit by such >> a bug. > > > Is there a way of not using those units from numpy, to check if > that's what's going on? I think that numpy only accesses the SSE units through ATLAS or other external library. So, build numpy without ATLAS. But I'm not 100% sure anymore if there aren't any optimizations that directly use SSE if it's available. > Or alternatively, how would I check if my program is messing with the > SSE bits? Hmm, I think that's a bit hairy. I'd suggest simply asking the C++ library's mailing list if they alter the error bits on the control registers of the SSE unit. (Out of curiousity, what library is it?) If you want hairy, though, I think you'd have to check from C with the appropriate calls -- I'd start with the source code in that bug report. It looks like they're inlining an assembly statement to query a SSE control register.

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Numpy x Matlab: some synthetic benchmarks
by Paulo Jose da Silva e Silva 11 Feb '07

11 Feb '07

Hello, Travis asked me to benchmark numpy versus matlab in some basic linear algebra operations. Here are the resuts for matrices/vectors of dimensions 5, 50 and 500: Operation x'*y x*y' A*x A*B A'*x Half 2in2 Dimension 5 Array 0.94 0.7 0.22 0.28 1.12 0.98 1.1 Matrix 7.06 1.57 0.66 0.79 1.6 3.11 4.56 Matlab 1.88 0.44 0.41 0.35 0.37 1.2 0.98 Dimension 50 Array 9.74 3.09 0.56 18.12 13.93 4.2 4.33 Matrix 81.99 3.81 1.04 19.13 14.58 6.3 7.88 Matlab 16.98 1.94 1.07 17.86 0.73 1.57 1.77 Dimension 500 Array 1.2 8.97 2.03 166.59 20.34 3.99 4.31 Matrix 17.95 9.09 2.07 166.62 20.67 4.11 4.45 Matlab 2.09 6.07 2.17 169.45 2.1 2.56 3.06 Obs: The operation Half is actually A*x using only the lower half of the matrix and vector. The operation 2in2 is A*x using only the even indexes. Of course there are many repetitions of the same operation: 100000 for dim 5 and 50 and 1000 for dim 500. The inner product is number of repetitions is multiplied by dimension (it is very fast). The software is numpy svn version 1926 Matlab 6.5.0.180913a Release 13 (Jun 18 2002) Both softwares are using the *same* BLAS and LAPACK (ATLAS for sse). As you can see, numpy array looks very competitive. The matrix class in numpy has too much overhead for small dimension though. This overhead is very small for medium size arrays. Looking at the results above (specially the small dimensions ones, for higher dimensions the main computations are being performed by the same BLAS) I believe we can say: 1) Numpy array is faster on usual operations but outerproduct (I believe the reason is that the dot function uses the regular matrix multiplication to compute outer-products, instead of using a special function. This can "easily" changes). In particular numpy was faster in matrix times vector operations, which is the most usual in numerical linear algebra. 2) Any operation that involves transpose suffers a very big penalty in numpy. Compare A'*x and A*x, it is 10 times slower. In contrast Matlab deals with transpose quite well. Travis is already aware of this and it can be probably solved. 3) When using subarrays, numpy is a slower. The difference seems acceptable. Travis, can this be improved? Best, Paulo Obs: Latter on (in a couple of days) I may present less synthetic benchmarks (a QR factorization and a Modified Cholesky).

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some work on arpack
by David Grant 22 Oct '06

22 Oct '06

Building an arpack extension turned out to be surprisingly simple. For example for dsaupd: f2py -c dsaupd.f -m dsaupd -L/usr/lib/blas/atlas:/usr/lib/lapack/atlas -llapack -lblas -larpack It took me a long time to get the command down to something that simple. Took me a while even to figure out I could just use the arpack library on my computer rather than re-linking all of arpack! I was able to import the dsaupd.so python module just fine and I was also able to call it just fine. I'll have to tweak the pyf file in order to get some proper output. But this gives me confidence that arpack is easy to hook into which is what others have said in the past, but without any experience with f2py I had no idea myself. f2py is awesome, for anyone who doesn't know. Matlab has interfaces for the arpack functions like dsaupd, dseupd, dnaupd, znaupd, zneupd (the mex file documentation claims those are the only ones, but they have more). Matlab has a C interface to these functions in arpackc.mex* and the script eigs.m does the grunt work, providing a very high-level interface as well as doing some linear algebra (the same type of stuff that is done in arpack's examples directory I gather) and various other things. My idea is (if I have time) to write an eigs-like function in python that will only perform a subset of what Matlab's eigs does for. It will, for example, compute a certain number of eigenvalues and eigenvectors for a real, sparse, symmetric matrix (the case I'm interested in)... I hope that this subset-of-matlab's-eigs function will not be too hard to write. Then more functionality can be added on to eigs.py later... Does this make sense? Has anyone else started work on arpack integration at all? -- David Grant http://www.davidgrant.ca

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Non-writeable default for numpy.ndarray
by Francesc Altet 01 Oct '06

01 Oct '06

Hello, Is the next a bug a feature? In [102]: f4=numpy.ndarray(buffer="a\x00b"*4, dtype="f4", shape=3) In [103]: f4 Out[103]: array([ 2.60561966e+20, 8.94319890e-39, 5.92050103e+20], dtype=float32) In [104]: f4[2] = 2 --------------------------------------------------------------------------- <type 'exceptions.RuntimeError'> Traceback (most recent call last) /home/faltet/python.nobackup/numpy/<ipython console> in <module>() <type 'exceptions.RuntimeError'>: array is not writeable In [105]: f4.flags.writeable = True In [106]: f4[2] = 2 In [107]: f4 Out[107]: array([ 2.60561966e+20, 8.94319890e-39, 2.00000000e+00], dtype=float32) i.e. in an array built from ndarray, the default is that it has to be read-only? -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

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return type diffences of indexed arrays with Intel C++ compiler (Python 2.5)
by Lars Bittrich 30 Sep '06

30 Sep '06

Hi all, recently I tried to compile numpy/scipy with Python 2.5 and Intel C++ compiler 9.1. At first everything was fine, but the scipy test produced a few errors. The reason was a little difference: latest svn(1.0.dev3233) with Intel C compiler (Python 2.5): ----------------------------------------------------------- In [1]:from numpy import ones, zeros, integer In [2]: In [2]:x = ones(1) In [3]:i = zeros(1, integer) In [4]:x[i] Out[4]:1.0 latest svn(1.0.dev3233) with GCC 3.3 (Python 2.3): -------------------------------------------------- In [1]:from numpy import ones, zeros, integer In [2]: In [2]:x = ones(1) In [3]:i = zeros(1, integer) In [4]:print x[i] Out[4]:array([1]) The Intel version gives me a scalar whereas the gcc version an array. Maybe Python 2.5 is causing this problem but my first guess was the compiler. A typical error message from scipy.test(10): ====================================================================== ERROR: check_chebyc (scipy.special.tests.test_basic.test_chebyc) ---------------------------------------------------------------------- Traceback (most recent call last): File "[...]/lib/python2.5/site-packages/scipy/special/tests/test_basic.py", line 667, in check_chebyc C1 = chebyc(1) File "[...]/lib/python2.5/site-packages/scipy/special/orthogonal.py", line 461, in chebyc p = orthopoly1d(x,w,hn,kn,wfunc=lambda x: 1.0/sqrt(1-x*x/4.0),limits=(-2,2),monic=monic) File "[...]/lib/python2.5/site-packages/scipy/special/orthogonal.py", line 78, in __init__ equiv_weights = [weights[k] / wfunc(roots[k]) for k in range(len(roots))] TypeError: object of type 'numpy.complex128' has no len() Does anyone know how to resolve the problem? Best regards, Lars

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Problem building from svn under cygwin
by Coatimundi 30 Sep '06

30 Sep '06

I have the latest cygwin environment and am using gcc 3.4.4. to build numpy. Configure works: python setup.py configure --compiler=mingw32 Build fails: python setup.py build --compiler=mingw32 While building numpy.random.mtrand I get this: _configtest.c:7:2: #error No _WIN32 and a little bit later the build aborts completely on multiarray with this: building 'numpy.core.multiarray' extension compiling C sources C compiler: gcc -mno-cygwin -mdll -O -Wall creating build/temp.cygwin-1.5.21-i686-2.5 creating build/temp.cygwin-1.5.21-i686-2.5/numpy creating build/temp.cygwin-1.5.21-i686-2.5/numpy/core creating build/temp.cygwin-1.5.21-i686-2.5/numpy/core/src compile options: '-Ibuild/src.cygwin-1.5.21-i686-2.5/numpy/core/src -Inumpy/core/include -Ibuild/src.cygwin-1.5.21-i686-2.5/numpy/core -Inumpy/core/src -Inumpy/core/include -I/usr/local/include/python2.5 -c' gcc -mno-cygwin -mdll -O -Wall -Ibuild/src.cygwin-1.5.21-i686-2.5/numpy/core/src -Inumpy/core/include -Ibuild/src.cygwin-1.5.21-i686-2.5/numpy/core -Inumpy/core/src -Inumpy/core/include -I/usr/local/include/python2.5 -c numpy/core/src/multiarraymodule.c -o build/temp.cygwin-1.5.21-i686-2.5/numpy/core/src/multiarraymodule.o In file included from /usr/local/include/python2.5/Python.h:57, from numpy/core/src/multiarraymodule.c:18: /usr/local/include/python2.5/pyport.h:226:24: sys/select.h: No such file or directory In file included from /usr/local/include/python2.5/Python.h:57, from numpy/core/src/multiarraymodule.c:18: /usr/local/include/python2.5/pyport.h:226:24: sys/select.h: No such file or directory I am puzzled, because /usr/include/sys/select.h actually does exist on my cygwin installation. But maybe that's not what pyport.h is actually looking for??? I've been banging away at this for several hours, so I must be doing something silly. Any help will be much appreciated. Thanks!

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Q: NumPy (svn) with cygwin build error?
by Kyle Ferrio 29 Sep '06

29 Sep '06

Hello. Starting from a freshly installed cygwin, I have built Python 2.5, ATLAS and LAPACK all from source using gcc 3.4.4 (cygming special) for both the C and f77 compilers. Now I am trying to build NumPy from SVN. I have edited site.cfg so that python setup.py config --compiler=mingw32 finds all of the linear algebra packs and completes without error. Then I fire off python setup.py build --compiler=mingw32 and I soon get (while building extension numpy.random.mtrand) _configtest.c:7:2: #error No _WIN32 which is not immediately fatal but definitely a concern. Soon after, building numpy.core.multiarray gives me this: building 'numpy.core.multiarray' extension compiling C sources C compiler: gcc -mno-cygwin -mdll -O -Wall creating build/temp.cygwin-1.5.21-i686-2.5 creating build/temp.cygwin-1.5.21-i686-2.5/numpy creating build/temp.cygwin-1.5.21-i686-2.5/numpy/core creating build/temp.cygwin-1.5.21-i686-2.5/numpy/core/src compile options: '-Ibuild/src.cygwin-1.5.21-i686-2.5/numpy/core/src -Inumpy/core/include -Ibuild/src.cygwin-1.5.21-i686-2.5/numpy/core -Inumpy/core/src -Inumpy/core/include -I/usr/local/include/python2.5 -c' gcc -mno-cygwin -mdll -O -Wall -Ibuild/src.cygwin-1.5.21-i686-2.5/numpy/core/src -Inumpy/core/include -Ibuild/src.cygwin-1.5.21-i686-2.5/numpy/core -Inumpy/core/src -Inumpy/core/include -I/usr/local/include/python2.5 -c numpy/core/src/multiarraymodule.c -o build/temp.cygwin-1.5.21-i686-2.5/numpy/core/src/multiarraymodule.o In file included from /usr/local/include/python2.5/Python.h:57, from numpy/core/src/multiarraymodule.c:18: /usr/local/include/python2.5/pyport.h:226:24: sys/select.h: No such file or directory In file included from /usr/local/include/python2.5/Python.h:57, from numpy/core/src/multiarraymodule.c:18: /usr/local/include/python2.5/pyport.h:226:24: sys/select.h: No such file or directory I am puzzzled, because /usr/include/sys/select.h does exist. But maybe that's not what pyport.h is actually looking for? I've been banging away at this for several hours, so I must be doing something silly. Any help will be much appreciated. Thanks! _________________________________________________________________ Express yourself - download free Windows Live Messenger themes! http://clk.atdmt.com/MSN/go/msnnkwme0020000001msn/direct/01/?href=http://im…

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Incorrect removal of NULL char in buffers
by Francesc Altet 29 Sep '06

29 Sep '06

Hi, I'm trying to build-up numpy arrays coming from buffers, and I'm getting a somewhat unexpected result. First, for numeric values, everything seems ok (i.e. the NULL character is correctly interpretated), and works equally for both numarray and numpy: In [98]: numarray.array("a\x00b"*4, dtype='Float32',shape=3) Out[98]: array([ 2.60561966e+20, 8.94319890e-39, 5.92050103e+20], type=Float32) In [99]: numpy.ndarray(buffer="a\x00b"*4, dtype='Float32',shape=3) Out[99]: array([ 2.60561966e+20, 8.94319890e-39, 5.92050103e+20], dtype=float32) However, for string values, numpy seems to work in a strange way. The numarray have an expected behaviour, IMO: In [100]: numarray.strings.array(buffer="a\x00b"*4, itemsize=4, shape=3) Out[100]: CharArray(['a', '', 'ba']) but numpy haven't: In [101]: numpy.ndarray(buffer="a\x00b"*4, dtype="S4", shape=3) Out[101]: array([aba, ba, bab], dtype='|S4') i.e. it seems like numpy is striping-off NULL chars before building the object and I don't think this is correct. Cheers, -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

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Negative values with unsigned data types problems
by Francesc Altet 29 Sep '06

29 Sep '06

Hi, I'm writing this here because the numpy Trac seems down: {{{ Oops... Trac detected an internal error: The Trac Environment needs to be upgraded. Run trac-admin /home/scipy/trac/numpy upgrade" }}} Well, it seems that there are inconsistencies on creating arrays coming from negative values using unsigned integers: In [41]: numpy.asarray([-1], dtype='uint8') Out[41]: array([255], dtype=uint8) In [42]: numpy.asarray([-1], dtype='uint16') Out[42]: array([65535], dtype=uint16) until here it's fine, but: In [43]: numpy.asarray([-1], dtype='uint32') --------------------------------------------------------------------------- <type 'exceptions.TypeError'> Traceback (most recent call last) /home/faltet/python.nobackup/numpy/<ipython console> in <module>() /usr/local/lib/python2.5/site-packages/numpy/core/numeric.py in asarray(a, dtype, order) 129 are converted to base class ndarray. 130 """ --> 131 return array(a, dtype, copy=False, order=order) 132 133 def asanyarray(a, dtype=None, order=None): <type 'exceptions.TypeError'>: long() argument must be a string or a number, not 'list' and the same happens with 'uint64'. My numpy version is 1.0.dev3216 Regards, -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

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return type diffences of indexed arrays with Intel C++ compiler (Python 2.5)
by Lars Bittrich 29 Sep '06

29 Sep '06

Hi all, recently I tried to compile numpy/scipy with Python 2.5 and Intel C++ compiler 9.1. At first everything was fine, but the scipy test produced a few errors. The reason was a little difference: latest svn(1.0.dev3233) with Intel C compiler (Python 2.5): ----------------------------------------------------------- In [1]:from numpy import ones, zeros, integer In [2]: In [2]:x = ones(1) In [3]:i = zeros(1, integer) In [4]:x[i] Out[4]:1.0 latest svn(1.0.dev3233) with GCC 3.3 (Python 2.3): -------------------------------------------------- In [1]:from numpy import ones, zeros, integer In [2]: In [2]:x = ones(1) In [3]:i = zeros(1, integer) In [4]:print x[i] Out[4]:array([1]) The Intel version gives me a scalar whereas the gcc version an array. Maybe Python 2.5 is causing this problem but my first guess was the compiler. A typical error message from scipy.test(10): ====================================================================== ERROR: check_chebyc (scipy.special.tests.test_basic.test_chebyc) ---------------------------------------------------------------------- Traceback (most recent call last): File "[...]/lib/python2.5/site-packages/scipy/special/tests/test_basic.py", line 667, in check_chebyc C1 = chebyc(1) File "[...]/lib/python2.5/site-packages/scipy/special/orthogonal.py", line 461, in chebyc p = orthopoly1d(x,w,hn,kn,wfunc=lambda x: 1.0/sqrt(1-x*x/4.0),limits=(-2,2),monic=monic) File "[...]/lib/python2.5/site-packages/scipy/special/orthogonal.py", line 78, in __init__ equiv_weights = [weights[k] / wfunc(roots[k]) for k in range(len(roots))] TypeError: object of type 'numpy.complex128' has no len() Does anyone know how to resolve the problem? Best regards, Lars

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storage order and concatenate
by David Cournapeau 29 Sep '06

29 Sep '06

Hi, I noticed a behaviour which I found counter-intuitive at least when using concatenate. I have a function which takes a numpy array of rank 2 as input, let's say foo(in): a = N.randn((10, 2)) foo(a) To test a ctype implementation of foo against the python version, my test has something like X1 = N.linspace(-2, 2, 10)[:, N.newaxis] X2 = N.linspace(-1, 3, 10)[:, N.newaxis] a = N.concatenate(([X1, X2]), 1) which has Fortran storage (column major order), where as creating a as a = N.zeros((10, 2)) a[:,0] = N.linspace(-2, 2, 10) a[:,1] = N.linspace(-1, 3, 10) has C storage (row major order). What are the rules concerning storage with numpy ? I thought it was always C, except if stated explicitly. I can obviously understand why concatenate gives a Fortran order from an implementation point of view, but this looks kind of strange to me, David

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32/64-bit machines, integer arrays and python ints
by Bill Spotz 29 Sep '06

29 Sep '06

I am wrapping code using swig and extending it to use numpy. One class method I wrap (let's call it myElements()) returns an array of ints, and I convert it to a numpy array with PyArray_SimpleNew(1,n,'i'); I obtain the data pointer, fill in the values and return it as the method return argument. In python, it is common to want to loop over this array and treat its elements as integers: for row in map.myElements(): matrix.setElements(row, [row-1,row,row+1], [-1.0,2.0,-1.0]) On a 32-bit machine, this has worked fine, but on a 64-bit machine, I get a type error: TypeError: in method 'setElements', argument 2 of type 'int' because row is a <type 'int32scalar'>. It would be nice if I could get the integer conversion to work automatically under the covers, but I'm not exactly sure how to make that work. ** Bill Spotz ** ** Sandia National Laboratories Voice: (505)845-0170 ** ** P.O. Box 5800 Fax: (505)284-5451 ** ** Albuquerque, NM 87185-0370 Email: wfspotz(a)sandia.gov **

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return value of negative power
by Stefan van der Walt 28 Sep '06

28 Sep '06

Hi all, Currently, the power function returns '0' for negative powers of integers: In [1]: N.power(3,-2) Out[1]: 0 (or, more confusingly) In [1]: N.power(a,b) Out[1]: 0 which is almost certainly not the answer you want. Two possible solutions may be to upcast the input to float before calculation, or to return nan. This would be consistent with a function like sqrt: In [10]: N.sqrt(3) Out[10]: 1.7320508075688772 In [11]: N.sqrt(-3) Out[11]: nan Does anyone have an opinion on whether the change is necessary, and if so, on which one would work best? Regards Stéfan

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how to compile numpy with visual-studio-2003?
by mg 28 Sep '06

28 Sep '06

Hello, I just download the newly Python-2.5 and Numpy-1.0rc1 and all work fine on Linux-x86 32 and 64bit platforms. Now, I try to compile the both distributions on WindowsXP with VisualStudio2003. No problem to compile Python-2.5, but i have some troubles with Numpy-1.0rc1 and I didn't find any help in the provided setup.py. So, does someone can tell me how to do it? Thanks, Mathieu.

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lexsort segfault sorting strings
by eric 28 Sep '06

28 Sep '06

I've been using the new record arrays and lexsort from numpy quite a lot lately. Very cool stuff. Using the nightly egg for numpy from here (I believe it is up to date...): http://code.enthought.com/enstaller/eggs/numpy-nightly-py2.4-win32.egg I get segfaults when using lexsort on character arrays. A lot of my columns in record arrays are string based, so sorting the arrays based on these columns would be really handy. Here is an example that crashes for me. <http://code.enthought.com/enstaller/eggs/numpy-nightly-py2.4-win32.egg>C:\wrk\mt\trunk\src\lib\mt\statement\tests>python Python 2.4.3 - Enthought Edition 1.0.0 (#69, Aug 2 2006, 12:09:59) [MSC v.1310 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> from numpy import lexsort >>> lst = [1,2,3] >>> lexsort((lst,)) array([0, 1, 2]) >>> lst = ['abc','cde','fgh'] >>> lexsort((lst,)) <seg-fault> Do others see this? I've opened a ticket at: http://projects.scipy.org/scipy/numpy/ticket/298 thanks, eric

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compiling ATLAS for numpy
by Emanuele Olivetti 28 Sep '06

28 Sep '06

Hi, I'm installing numpy on a 2 cpus intel pentium 4 Linux box. I'm installing BLAS and LAPACK from sources too and I need to tune compiler flags. Here is the question: which are the proper flags for compiling LAPACK? Inside lapack.tgz make.inc.LINUX says: OPTS = -funroll-all-loops -fno-f2c -O3 instead on scipy.org[0] it's suggested: OPTS = -O2 I assume that using -O3 and unrolling loops should have definitely better performance that just -O2 but I'm wondering if the speedy options can be a problem for current numpy release (numpy-1.0rc1). Is it safe to use '-funroll-all-loops -fno-f2c -O3'? Thanks in advance for answers, Emanuele [0]: http://scipy.org/Installing_SciPy P.S.: my GCC is v3.4.5

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streetcar
by Neddie Cunningham 28 Sep '06

28 Sep '06

Okay, so not quite as much as those people doing shows every night of the week, but it's still been really busy here. And where is their remembrance? The city becomes awash with media-obsessed types, and it's difficult not to get caught up in the hysteria. Certainly, yesterday I felt really quite fragile, and didn't get much done, except for a quick visit to the supermarket for supplies during the afternoon. I was blown away by some truely amazing singing, and some rather fine acting too. Some of their number are modestly decent musicians, and can actually hold a tune. To help track that proces. She's also quite a creative person, who likes using self-made drawing to illustrate her musings. : Drake Gold Resources Inc. I've been watching a bit of TV, reading a bit, and surfing the net for the rest of time. Even with the usual summer lull, there's still been a steady flow of decent jobs over the last few weeks, which indicates a fairly bouyant employment market. But the others obviously carried on without me, and were quite drunk by the time I met up with them again a few hours later. Luckily, I had arrived a good ten minutes early, and had a great spot right at the front. It felt a bit strange to have the bar open for normal business while filming was in progress, but also interesting for us to be in the midst of all this action. But the web-based review sites don't have that limitation, and so there seems little excuse for not publishing the promised reviews. Since then, I've perked up a bit; that's despite having received another job rejection letter. But even without an amorous advance in your direction, you'll leave with a big grin on your face. But even without an amorous advance in your direction, you'll leave with a big grin on your face. And so, by the time we were thrown out at closing time, I was pissed again. Sure, it'll be great to have a rest. : Drake Gold Resources Inc. She's also quite a creative person, who likes using self-made drawing to illustrate her musings. It's an obsessive practice that I think a lot of amateur Fringe performers get dragged into. In all, I've sung in a total of four different concerts and five different church services in the last month. This departing friend had organised his final farewell drinks in one of Edinburgh's most famous public houses. And that's certainly dented my confidence somewhat.

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Release candidate 2.0 will come out mid-week next week
by Travis Oliphant 28 Sep '06

28 Sep '06

Hi all, I'd like to release numpy 1.0rc2 on about October 5 of next week. Then, the release of numpy 1.0 official should happen on Monday, October 17. Please try and get all fixes and improvements in before then. Backward-incompatible changes are not acceptable at this point (unless they are minor or actually bug-fixes). I think NumPy has been cooking long enough. Any remaining problems can be fixed with maintenance releases. When 1.0 comes out, we will make a 1.0 release branch where bug-fixes should go as well as on the main trunk (I'd love for a way to do that automatically). There are lots of projects that need to start converting to NumPy 1.0 if we are going to finally have a "merged" community. The actual release of NumPy 1.0 will indicate that we are now committing to stability. Thanks to all that have been contributing so much to the project. -Travis

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Question answered incorrectly at NumPy Tutorial
by Travis Oliphant 28 Sep '06

28 Sep '06

During my NumPy Tutorial at the SciPy conference last month, somebody asked the question about the memory requirements of index arrays that I gave the wrong impression about. Here is the context and the correct response that should alleviate concerns about large cross-product index arrays. I was noting how copy-based (advanced) indexing using index arrays works in multiple-dimensions by creating an array of the same-shape of the input index arrays constructed by selecting the elements indicated by respective elements of the index arrays. If a is 2-d, then a[[10,12,14],[13, 15, 17]] returns a 1-d array with elements [a[10,13], a[12,15], a[14,17]]. This is *not* the cross-product that some would expect. The cross-product can be generated using the ix_ function a[ix_([10,12,14], [13,15,17])] is equivalent to a[[[10,10,10],[12,12,12],[14,14,14]], [[13,15,17],[13,15,17],[13,15,17]]] which will return [[a[10,13] a[10,15], a[10,17]], [a[12,13] a[12,15], a[12,17]], [a[14,13] a[14,15], a[14,17]]] The concern mentioned at the conference was that the cross-product would generate large intermediate index arrays for large input arrays to ix_. At the time, I think I validated the concern. However, the concern is unfounded. This is because the cross product function does not actually create a large intermediate array, but uses the broad-casting implementation of indexing to generate the 2-d indexing array "on-the-fly" (much like ogrid and other tools in NumPy). Notice: ix_([10,12,14], [13,15,17]) (array([[10], [12], [14]]), array([[13, 15, 17]])) The first indexing array is 3x1, while the second is 1x3. The result array will be 3x3, but the 2-d indexing array is never actually stored. Just to set my mind at ease about possible mis-information I spread during the tutorial, and give a little tutorial on advanced indexing. Best, -Travis

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should I replace asarray with asanyarray in my code ?
by Sebastian Haase 28 Sep '06

28 Sep '06

Hi, This is a vaguely formulated question ... When I work with memmap'ed files/arrays I have a derived class that adds special attributes to the array class (referring to the MRC image file format used in medical / microscopy imaging) What are the pros and cons for asarray() vs. asanyarray() One obvious con for asanyarray is that its longer and asarray is what I have been using for the last few years ;-) Thanks, Sebastian

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More about data types in NumPy
by Francesc Altet 27 Sep '06

27 Sep '06

Hello, Sorry for being insistent, but I recognize that I'm having a bad time with NumPy data type rational. Is there an explanation for this?: >>> numpy.dtype('i4').type <type 'numpy.int32'> >>> numpy.dtype('int32').type <type 'numpy.int32'> >>> numpy.dtype('i4').type == numpy.dtype('int32').type True So far so good, but is the next the intended behaviour? >>> numpy.typeDict['i4'] <type 'numpy.int32'> >>> numpy.typeDict['int32'] <type 'numpy.int32'> >>> numpy.typeDict['i4'] == numpy.typeDict['int32'] False -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

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Re: [Numpy-discussion] Segfault with 64-bit, ACML, Python 2.5
by Nickolas V Fotopoulos 27 Sep '06

27 Sep '06

On Tuesday, September 26, Robert Kern wrote: > Please run numpy.test(10,10). That will print out the name of the test before > running it. Thus we will find out which test is segfaulting. Robert, The last line is: Check creation from list of list of tuplesSegmentation fault This matches what gdb said. I wouldn't think there any BLAS or LAPACK calls during array creation, so that would rule out ACML as the culprit. Any quick ideas of where specifically to look? Chuck, I had a rather different setup working. I changed too many variables (python 2.4, no ACML, pre-1.0beta SVN checkout to python 2.5, ACML, yesterday SVN checkout) to be sure what is breaking. I'll regress a bit and see if I can find out what's wrong. I always remove the build directory before reinstalling, but I generally don't remove the site-packages/numpy directory. I'll start doing that. I'm fairly sure I'm not picking up 2.4's site-packages. Oh, another thing is that I had trouble with the gnu (g77) version of the ACML libraries, so I forced gfortran usage with .pydistutils.cfg. Should that break things? Thanks, Nick

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numpy 1.0rc1 has problems finding blas
by Hanno Klemm 27 Sep '06

27 Sep '06

Hello, I try to build numpy 1.0rc1 with blas and lapack installed in another place than /usr/lib. I compiled blas and lapack according to the instructions on the scipy webpage and installed them under /scratch/python2.4/lib. I set the environment variables BLAS=/scratch/python2.4/lib/libfblas.a and LAPACK=/scratch/python2.4/lib/libflapack.a however, if I run python setup.py config in the numpy source directory, it appears only to pick up the libraries installed under /usr/lib, which are incomplete. What else can I do to help numpy finding the right libraries? Regards, Hanno -- Hanno Klemm klemm(a)phys.ethz.ch

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Certified
by ofert 27 Sep '06

27 Sep '06

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