Mailman 3 September 2004 - NumPy-Discussion

PyArray_ContiguousFromObject & PyDECREF
by Michiel Jan Laurens de Hoon May 16, 2011

May 16, 2011

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

3 2

floating point exception weirdness
by Andrew Straw Nov. 3, 2004

Nov. 3, 2004

Hi All, I've isolated numarray.ieeespecial as the cause of a strange error which I posted on c.l.py: http://groups.google.com/groups?dq=&hl=en&lr=&ie=UTF-8&threadm=41198B29.709… My non-expert guess as to an explanation: some statements early in numarray.ieeespecial (e.g. _na.array(1.0)/_na.array(0.0)) result in a floating point exception being set which lies dormant until I call certain functions in the Intel IPP library (e.g. ippiAddWeighted_8u32f_C1IR), at which point the latent exception bit my Python program is killed while the console says "Floating point exception". I've come to this tentative conclusion based on 3 minimal Pyrex programs: ---------------------------------------------------- Version 1: import numarray.numarrayall as _na plus_inf = inf = _na.array(1.0)/_na.array(0.0) cimport avg avg.work() # calls IPP, terminates with "Floating point exception". ---------------------------------------------------- Version 2: import numarray.numarrayall as _na # Define *ieee special values* _na.Error.pushMode(all="ignore") plus_inf = inf = _na.array(1.0)/_na.array(0.0) _na.Error.popMode() cimport avg avg.work() # calls IPP, terminates with "Floating point exception". ---------------------------------------------------- Version 3: import numarray.numarrayall as _na cimport avg avg.work() # calls IPP, terminates normally In the short term, I'll try and work around this problem by not importing numarray.ieeespecial, but in the long term, I think we should try and fix this. I'm happy to try to debug further if someone gives me advice on where to go from here. Cheers! Andrew

2 6

dot!=matrixmultiply bug when dotblas is present
by Fernando Perez Oct. 1, 2004

Oct. 1, 2004

Hi all, I found something today a bit unpleasant: if you install numeric without any BLAS support, 'matrixmultiply is dot==True', so they are fully interchangeable. However, to my surprise, if you build numeric with the blas optimizations, they are NOT identical. The reason is a bug in Numeric.py. After defining dot, the code reads: #This is obsolete, don't use in new code matrixmultiply = dot and at the very end of the file, we have: # try to import blas optimized dot, innerproduct and vdot, if available try: from dotblas import dot, innerproduct, vdot except ImportError: pass Obviously this means that matrixmultiply is stuck with the _old_ definition of dot, and does not benefit from the blas optimizations. This is BAD, as for a 1024x1024 matrix the difference is staggering: planck[Numeric]> pylab In [1]: a=rand(1024,1024) In [2]: b=rand(1024,1024) In [3]: from IPython.genutils import timing In [4]: timing 1,dot,a,b ------> timing(1,dot,a,b) Out[4]: 0.55591500000000005 In [5]: timing 1,matrixmultiply,a,b ------> timing(1,matrixmultiply,a,b) Out[5]: 68.142640999999998 In [6]: _/__ Out[6]: 122.57744619231356 Pretty significant difference... The fix is trivial. In Numeric.py, at the very end of the file, this part: # try to import blas optimized dot, innerproduct and vdot, if available try: from dotblas import dot, innerproduct, vdot except ImportError: pass should read instead: # try to import blas optimized dot, innerproduct and vdot, if available try: from dotblas import dot, innerproduct, vdot matrixmultiply = dot #### <<<--- NEW LINE except ImportError: pass I just checked and the problem still exists in Numpy 23.4. Cheers, f

3 3

Re: [Numpy-discussion] random number facilities in numarray and main Python libs
by Bruce Southey Sept. 30, 2004

Sept. 30, 2004

Hi, The R project (http://www.r-project.org) provides a standalone GPL'ed Math library (buried in the src/nmath/standalone subdirectory of the R code). This includes random number generators for various distributions amongst other goodies. But I have not looked to see what approach that the actual uniform random generator uses (source comment says "A version of Marsaglia-MultiCarry"). However, this library should be as least as good as and probably better than Ranlib that is currently being used I used SWIG to generate wrappers for most of the functions (except the voids). SWIG makes it very easy but I needed to create a SWIG include file because using the header alone did not work correctly. If anyone wants more information or files, let me know. Bruce Southey ---- Original message ---- >Date: Tue, 07 Sep 2004 16:35:11 -0700 >From: Robert Kern <rkern(a)ucsd.edu> >Subject: Re: [Numpy-discussion] random number facilities in numarray and main Python libs >To: numpy-discussion(a)lists.sourceforge.net > >Faheem Mitha wrote: >[snip] >> Are the random number facilities provided by numarray.random_array >> superior to those provided to those provided by the random module in the >> Python library? They certainly seem more extensive, and I like the >> interface better. >> >> If so, why not replace the random module by the equivalent functionality >> from numarray.random_array, and have everyone use the same random number >> generator? Or is this impossible for practical reasons? > >numarray.random_array can generate arrays full of random numbers. >Standard Python's random does not and will not until numarray is part of >the standard library. Standard Python's random also uses the Mersenne >Twister algorithm which is, by most accounts, superior to RANLIB's >algorithm, so I for one would object to replacing it with numarray's >code. :-) > >I do intend to implement the Mersenne Twister algorithm for SciPy's PRNG >facilities (on some unspecified weekend). I will also try to code >something up for numarray, too. > >> By the way, what is the name of the pseudo-random number generator being >> used? I see that the code is in Packages/RandomArray2/Src, but could not >> see where the name of the generator is mentioned. > ><URL:http://orion.math.iastate.edu/burkardt/c_src/ranlib/ranlib_base.txt> >documents the base algorithm. > >> Faheem. > >-- >Robert Kern >rkern(a)ucsd.edu > >"In the fields of hell where the grass grows high > Are the graves of dreams allowed to die." > -- Richard Harter > > >------------------------------------------------------- >This SF.Net email is sponsored by BEA Weblogic Workshop >FREE Java Enterprise J2EE developer tools! >Get your free copy of BEA WebLogic Workshop 8.1 today. >http://ads.osdn.com/?ad_id=5047&alloc_id=10808&op=click >_______________________________________________ >Numpy-discussion mailing list >Numpy-discussion(a)lists.sourceforge.net >https://lists.sourceforge.net/lists/listinfo/numpy-discussion

2 2

random number facilities in numarray and main Python libs
by Faheem Mitha Sept. 28, 2004

Sept. 28, 2004

Dear People, I recently encountered a strange problem when writing some Python using random number code. I tried setting the seed at top level, but I was still get random results. I submitted a bug report, and had my error explained to me. I had run into this problem several times, and I think some mental block had prevented me from seeing the source of the problem. Part of the reason is that I had previously been using R, and everyone uses the same random number generator facilities. Are the random number facilities provided by numarray.random_array superior to those provided to those provided by the random module in the Python library? They certainly seem more extensive, and I like the interface better. If so, why not replace the random module by the equivalent functionality from numarray.random_array, and have everyone use the same random number generator? Or is this impossible for practical reasons? By the way, what is the name of the pseudo-random number generator being used? I see that the code is in Packages/RandomArray2/Src, but could not see where the name of the generator is mentioned. Faheem.

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RE: [Numpy-discussion] Problems with installation of version 23.3
by Nadav Horesh Sept. 27, 2004

Sept. 27, 2004

The problem is indicated in the following line: Packages/dotblas/dotblas/_dotblas.c:7:19: cblas.h: No such file or directory I have this file (cblas.h) as a part of ATLAS installation Nadav -----Original Message----- From: Nils Wagner [mailto:nwagner@mecha.uni-stuttgart.de] Sent: Mon 27-Sep-04 10:46 To: numpy-discussion(a)lists.sourceforge.net Cc: Subject: [Numpy-discussion] Problems with installation of version 23.3 Hi all. I tried to install Numeric-23.3, but it failed. Here is the output python2.3 setup.py install running install running build running build_py running build_ext building 'lapack_lite' extension gcc -pthread -shared build/temp.linux-i686-2.3/Src/lapack_litemodule.o -L/usr/local/lib/atlas -llapack -lcblas -lf77blas -latlas -lg2c -o build/lib.linux-i686-2.3/lapack_lite.so building 'FFT.fftpack' extension creating build/temp.linux-i686-2.3/Packages creating build/temp.linux-i686-2.3/Packages/FFT creating build/temp.linux-i686-2.3/Packages/FFT/Src gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/FFT/Src/fftpack.c -o build/temp.linux-i686-2.3/Packages/FFT/Src/fftpack.o gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/FFT/Src/fftpackmodule.c -o build/temp.linux-i686-2.3/Packages/FFT/Src/fftpackmodule.o gcc -pthread -shared build/temp.linux-i686-2.3/Packages/FFT/Src/fftpackmodule.o build/temp.linux-i686-2.3/Packages/FFT/Src/fftpack.o -o build/lib.linux-i686-2.3/FFT/fftpack.so building 'RNG.RNG' extension creating build/temp.linux-i686-2.3/Packages/RNG creating build/temp.linux-i686-2.3/Packages/RNG/Src gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/RNG/Src/pmath_rng.c -o build/temp.linux-i686-2.3/Packages/RNG/Src/pmath_rng.o gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/RNG/Src/ranf.c -o build/temp.linux-i686-2.3/Packages/RNG/Src/ranf.o gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/RNG/Src/RNGmodule.c -o build/temp.linux-i686-2.3/Packages/RNG/Src/RNGmodule.o gcc -pthread -shared build/temp.linux-i686-2.3/Packages/RNG/Src/RNGmodule.o build/temp.linux-i686-2.3/Packages/RNG/Src/ranf.o build/temp.linux-i686-2.3/Packages/RNG/Src/pmath_rng.o -o build/lib.linux-i686-2.3/RNG/RNG.so building '_dotblas' extension creating build/temp.linux-i686-2.3/Packages/dotblas creating build/temp.linux-i686-2.3/Packages/dotblas/dotblas gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/dotblas/dotblas/_dotblas.c -o build/temp.linux-i686-2.3/Packages/dotblas/dotblas/_dotblas.o Packages/dotblas/dotblas/_dotblas.c:7:19: cblas.h: No such file or directory Packages/dotblas/dotblas/_dotblas.c: In function `FLOAT_dot': Packages/dotblas/dotblas/_dotblas.c:18: warning: implicit declaration of function `cblas_sdot' Packages/dotblas/dotblas/_dotblas.c: In function `DOUBLE_dot': Packages/dotblas/dotblas/_dotblas.c:23: warning: implicit declaration of function `cblas_ddot' Packages/dotblas/dotblas/_dotblas.c: In function `CFLOAT_dot': Packages/dotblas/dotblas/_dotblas.c:28: warning: implicit declaration of function `cblas_cdotu_sub' Packages/dotblas/dotblas/_dotblas.c: In function `CDOUBLE_dot': Packages/dotblas/dotblas/_dotblas.c:34: warning: implicit declaration of function `cblas_zdotu_sub' Packages/dotblas/dotblas/_dotblas.c: In function `dotblas_matrixproduct': Packages/dotblas/dotblas/_dotblas.c:150: warning: implicit declaration of function `cblas_daxpy' Packages/dotblas/dotblas/_dotblas.c:154: warning: implicit declaration of function `cblas_saxpy' Packages/dotblas/dotblas/_dotblas.c:158: warning: implicit declaration of function `cblas_zaxpy' Packages/dotblas/dotblas/_dotblas.c:162: warning: implicit declaration of function `cblas_caxpy' Packages/dotblas/dotblas/_dotblas.c:190: warning: implicit declaration of function `cblas_dgemv' Packages/dotblas/dotblas/_dotblas.c:190: error: `CblasRowMajor' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c:190: error: (Each undeclared identifier is reported only once Packages/dotblas/dotblas/_dotblas.c:190: error: for each function it appears in.) Packages/dotblas/dotblas/_dotblas.c:191: error: `CblasNoTrans' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c:196: warning: implicit declaration of function `cblas_sgemv' Packages/dotblas/dotblas/_dotblas.c:202: warning: implicit declaration of function `cblas_zgemv' Packages/dotblas/dotblas/_dotblas.c:208: warning: implicit declaration of function `cblas_cgemv' Packages/dotblas/dotblas/_dotblas.c:218: error: `CblasTrans' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c:244: warning: implicit declaration of function `cblas_dgemm' Packages/dotblas/dotblas/_dotblas.c:251: warning: implicit declaration of function `cblas_sgemm' Packages/dotblas/dotblas/_dotblas.c:258: warning: implicit declaration of function `cblas_zgemm' Packages/dotblas/dotblas/_dotblas.c:265: warning: implicit declaration of function `cblas_cgemm' Packages/dotblas/dotblas/_dotblas.c: In function `dotblas_innerproduct': Packages/dotblas/dotblas/_dotblas.c:463: error: `CblasRowMajor' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c:464: error: `CblasNoTrans' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c:517: error: `CblasTrans' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c: In function `dotblas_vdot': Packages/dotblas/dotblas/_dotblas.c:652: warning: implicit declaration of function `cblas_zdotc_sub' Packages/dotblas/dotblas/_dotblas.c:656: warning: implicit declaration of function `cblas_cdotc_sub' error: command 'gcc' failed with exit status 1 lisa:/var/tmp/Numeric-23.3 # Any suggestion would be appreicated. Nils locate cblas.h /usr/local/lib/atlas/cblas.h ------------------------------------------------------- This SF.Net email is sponsored by: YOU BE THE JUDGE. Be one of 170 Project Admins to receive an Apple iPod Mini FREE for your judgement on who ports your project to Linux PPC the best. Sponsored by IBM. Deadline: Sept. 24. Go here: http://sf.net/ppc_contest.php _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

2 1

Problems with installation of version 23.3
by Nils Wagner Sept. 26, 2004

Sept. 26, 2004

Hi all. I tried to install Numeric-23.3, but it failed. Here is the output python2.3 setup.py install running install running build running build_py running build_ext building 'lapack_lite' extension gcc -pthread -shared build/temp.linux-i686-2.3/Src/lapack_litemodule.o -L/usr/local/lib/atlas -llapack -lcblas -lf77blas -latlas -lg2c -o build/lib.linux-i686-2.3/lapack_lite.so building 'FFT.fftpack' extension creating build/temp.linux-i686-2.3/Packages creating build/temp.linux-i686-2.3/Packages/FFT creating build/temp.linux-i686-2.3/Packages/FFT/Src gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/FFT/Src/fftpack.c -o build/temp.linux-i686-2.3/Packages/FFT/Src/fftpack.o gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/FFT/Src/fftpackmodule.c -o build/temp.linux-i686-2.3/Packages/FFT/Src/fftpackmodule.o gcc -pthread -shared build/temp.linux-i686-2.3/Packages/FFT/Src/fftpackmodule.o build/temp.linux-i686-2.3/Packages/FFT/Src/fftpack.o -o build/lib.linux-i686-2.3/FFT/fftpack.so building 'RNG.RNG' extension creating build/temp.linux-i686-2.3/Packages/RNG creating build/temp.linux-i686-2.3/Packages/RNG/Src gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/RNG/Src/pmath_rng.c -o build/temp.linux-i686-2.3/Packages/RNG/Src/pmath_rng.o gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/RNG/Src/ranf.c -o build/temp.linux-i686-2.3/Packages/RNG/Src/ranf.o gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/RNG/Src/RNGmodule.c -o build/temp.linux-i686-2.3/Packages/RNG/Src/RNGmodule.o gcc -pthread -shared build/temp.linux-i686-2.3/Packages/RNG/Src/RNGmodule.o build/temp.linux-i686-2.3/Packages/RNG/Src/ranf.o build/temp.linux-i686-2.3/Packages/RNG/Src/pmath_rng.o -o build/lib.linux-i686-2.3/RNG/RNG.so building '_dotblas' extension creating build/temp.linux-i686-2.3/Packages/dotblas creating build/temp.linux-i686-2.3/Packages/dotblas/dotblas gcc -pthread -fno-strict-aliasing -DNDEBUG -D_FILE_OFFSET_BITS=64 -DHAVE_LARGEFILE_SUPPORT -O2 -march=i586 -mcpu=i686 -fmessage-length=0 -Wall -fPIC -I/usr/include/atlas -IInclude -IPackages/FFT/Include -IPackages/RNG/Include -I/usr/include/python2.3 -c Packages/dotblas/dotblas/_dotblas.c -o build/temp.linux-i686-2.3/Packages/dotblas/dotblas/_dotblas.o Packages/dotblas/dotblas/_dotblas.c:7:19: cblas.h: No such file or directory Packages/dotblas/dotblas/_dotblas.c: In function `FLOAT_dot': Packages/dotblas/dotblas/_dotblas.c:18: warning: implicit declaration of function `cblas_sdot' Packages/dotblas/dotblas/_dotblas.c: In function `DOUBLE_dot': Packages/dotblas/dotblas/_dotblas.c:23: warning: implicit declaration of function `cblas_ddot' Packages/dotblas/dotblas/_dotblas.c: In function `CFLOAT_dot': Packages/dotblas/dotblas/_dotblas.c:28: warning: implicit declaration of function `cblas_cdotu_sub' Packages/dotblas/dotblas/_dotblas.c: In function `CDOUBLE_dot': Packages/dotblas/dotblas/_dotblas.c:34: warning: implicit declaration of function `cblas_zdotu_sub' Packages/dotblas/dotblas/_dotblas.c: In function `dotblas_matrixproduct': Packages/dotblas/dotblas/_dotblas.c:150: warning: implicit declaration of function `cblas_daxpy' Packages/dotblas/dotblas/_dotblas.c:154: warning: implicit declaration of function `cblas_saxpy' Packages/dotblas/dotblas/_dotblas.c:158: warning: implicit declaration of function `cblas_zaxpy' Packages/dotblas/dotblas/_dotblas.c:162: warning: implicit declaration of function `cblas_caxpy' Packages/dotblas/dotblas/_dotblas.c:190: warning: implicit declaration of function `cblas_dgemv' Packages/dotblas/dotblas/_dotblas.c:190: error: `CblasRowMajor' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c:190: error: (Each undeclared identifier is reported only once Packages/dotblas/dotblas/_dotblas.c:190: error: for each function it appears in.) Packages/dotblas/dotblas/_dotblas.c:191: error: `CblasNoTrans' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c:196: warning: implicit declaration of function `cblas_sgemv' Packages/dotblas/dotblas/_dotblas.c:202: warning: implicit declaration of function `cblas_zgemv' Packages/dotblas/dotblas/_dotblas.c:208: warning: implicit declaration of function `cblas_cgemv' Packages/dotblas/dotblas/_dotblas.c:218: error: `CblasTrans' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c:244: warning: implicit declaration of function `cblas_dgemm' Packages/dotblas/dotblas/_dotblas.c:251: warning: implicit declaration of function `cblas_sgemm' Packages/dotblas/dotblas/_dotblas.c:258: warning: implicit declaration of function `cblas_zgemm' Packages/dotblas/dotblas/_dotblas.c:265: warning: implicit declaration of function `cblas_cgemm' Packages/dotblas/dotblas/_dotblas.c: In function `dotblas_innerproduct': Packages/dotblas/dotblas/_dotblas.c:463: error: `CblasRowMajor' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c:464: error: `CblasNoTrans' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c:517: error: `CblasTrans' undeclared (first use in this function) Packages/dotblas/dotblas/_dotblas.c: In function `dotblas_vdot': Packages/dotblas/dotblas/_dotblas.c:652: warning: implicit declaration of function `cblas_zdotc_sub' Packages/dotblas/dotblas/_dotblas.c:656: warning: implicit declaration of function `cblas_cdotc_sub' error: command 'gcc' failed with exit status 1 lisa:/var/tmp/Numeric-23.3 # Any suggestion would be appreicated. Nils locate cblas.h /usr/local/lib/atlas/cblas.h

1 0

ANN: F2PY - Fortran to Python Interface Generator
by Pearu Peterson Sept. 25, 2004

Sept. 25, 2004

F2PY - Fortran to Python Interface Generator -------------------------------------------- I am pleased to announce the eight public release of F2PY, version 2.43.239_1806. The purpose of the F2PY project is to provide the connection between Python and Fortran programming languages. For more information, see http://cens.ioc.ee/projects/f2py2e/ Download: http://cens.ioc.ee/projects/f2py2e/2.x/F2PY-2-latest.tar.gz http://cens.ioc.ee/projects/f2py2e/2.x/F2PY-2-latest.win32.exe http://cens.ioc.ee/projects/f2py2e/2.x/scipy_distutils-latest.tar.gz http://cens.ioc.ee/projects/f2py2e/2.x/scipy_distutils-latest.win32.exe What's new? ------------ * Added support for ``ENTRY`` statement. * New attributes: ``intent(callback)`` to support non-external Python calls from Fortran; ``intent(inplace)`` to support in-situ changes, including typecode and contiguouness changes, of array arguments. * Added support for ``ALLOCATABLE`` string arrays. * New command line switches: --compiler and --include_paths. * Numerous bugs are fixed. Support for ``PARAMETER``s has been improved considerably. * Documentation updates. Pyfort and F2PY comparison. Projects using F2PY, users feedback, etc. * Support for Numarray 1.1 (thanks to Todd Miller). * Win32 installers for F2PY and the latest scipy_distutils are provided. Enjoy, Pearu Peterson --------------- <P><A HREF="http://cens.ioc.ee/projects/f2py2e/">F2PY 2.43.239_1806</A> - The Fortran to Python Interface Generator (25-Sep-04)

1 0

Assignment from a list is slow in Numarray
by Timo Korvola Sept. 22, 2004

Sept. 22, 2004

Hello, I am new to the list, sorry if you've been through this before. I am trying to do some FEM computations using Petsc, to which I have written Python bindings using Swig. That involves passing arrays around, which I found delightfully simple with NA_{Input,Output,Io}Array. Numeric seems more difficult for output and bidirectional arrays. My code for reading a triangulation from a file went roughly like this: coord = zeros( (n_vertices, 2), Float) for v in n_vertices: coord[ v, :] = [float( s) for s in file.readline().split()] This was taking quite a bit of time with ~50000 vertices and ~100000 elements, for which three integers per element are read in a similar manner. I found it was faster to loop explicitly: coord = zeros( (n_vertices, 2), Float) for v in n_vertices: for j, c in enumerate( [float( s) for s in file.readline().split()]): coord[ v, j] = c Morally this uglier code with an explicit loop should not be faster but it is with Numarray. With Numeric assignment from a list has reasonable performance. How can it be improved for Numarray? -- Timo Korvola <URL:http://www.iki.fi/tkorvola>

3 5

Conference presentations
by Travis Oliphant Sept. 22, 2004

Sept. 22, 2004

Hi all, The SciPy 2004 conference was a great success. I personally enjoyed seeing all attendees and finding out about the activity that has been occurring with Python and Science. As promised, all of the presentations that were submitted to abstracts(a)scipy.org are now available on-line under the conference-schedule page. The link is http://www.scipy.org/wikis/scipy04/ConferenceSchedule If anyone who hasn't submitted their presentation would like to, you still can. As I was only able to attend the first day, I cannot comment on the entire conference. However, what I saw was very encouraging. There continues to be a great amount of work being done in using Python for Scientific Computing and the remaining problems seems to be how to get the word out and increase the user base. Many thanks are due to the presenters and the conference sponsors: *The National Biomedical Computation Resource* <http://nbcr.sdsc.edu> (NBCR, UCSD, San Diego, CA) The mission of the National Biomedical Computation Resource at the University of California San Diego and partners at The Scripps Research Institute and Washington University is to conduct, catalyze, and advance biomedical research by harnessing, developing and deploying forefront computational, information, and grid technologies. NBCR is supported by _National Institutes of Health (NIH) _ through a _National Center for Research Resources_ centers grant (P 41 RR08605). *The Center for Advanced Computing Research* <http://www.cacr.caltech.edu/> (CACR, CalTech <http://www.scipy.org/wikis/scipy04/CalTech>, Pasadena, CA) CACR is dedicated to the pursuit of excellence in the field of high-performance computing, communication, and data engineering. Major activities include carrying out large-scale scientific and engineering applications on parallel supercomputers and coordinating collaborative research projects on high-speed network technologies, distributed computing and database methodologies, and related topics. Our goal is to help further the state of the art in scientific computing. *Enthought, Inc.* <http://www.enthought.com> (Austin, TX) Enthought, Inc. provides business and scientific computing solutions through software development, consulting and training. Best regards to all, -Travis Oliphant Brigham Young University 459 CB Provo, UT 84602 oliphant.travis(a)ieee.org

2 1

A bug in creating array of long int.
by Shin Sept. 20, 2004

Sept. 20, 2004

I got the following strange value when converting a long integer into numarray. I spent some time in tracking it down in my program. Is it a bug or an expected one? Thanks. >>> array([True]) array([1], type=Bool) >>> array([1]) array([1]) >>> array([1L]) # How about preserving long int type? array([1]) >>> array([10000000000000000L]) array([1874919424]) # Oops. The value is changed without any notice. -- Daehyok Shin (Peter)

3 3

RE: [Numpy-discussion] Assignment from a list is slow in Numarray
by Nadav Horesh Sept. 19, 2004

Sept. 19, 2004

Yo may try the aging module TableIO (http://php.iupui.edu/~mmiller3/python/). Just replace "import Numeric" by "import numarray as Numeric". It may give up to two-fold speed improvement. Since you have C skills, you might update the small C source to interact directly with numarray. Nadav -----Original Message----- From: Timo Korvola [mailto:tkorvola@e.math.helsinki.fi] Sent: Sun 19-Sep-04 20:35 To: numpy-discussion(a)lists.sourceforge.net Cc: Subject: [Numpy-discussion] Assignment from a list is slow in Numarray Hello, I am new to the list, sorry if you've been through this before. I am trying to do some FEM computations using Petsc, to which I have written Python bindings using Swig. That involves passing arrays around, which I found delightfully simple with NA_{Input,Output,Io}Array. Numeric seems more difficult for output and bidirectional arrays. My code for reading a triangulation from a file went roughly like this: coord = zeros( (n_vertices, 2), Float) for v in n_vertices: coord[ v, :] = [float( s) for s in file.readline().split()] This was taking quite a bit of time with ~50000 vertices and ~100000 elements, for which three integers per element are read in a similar manner. I found it was faster to loop explicitly: coord = zeros( (n_vertices, 2), Float) for v in n_vertices: for j, c in enumerate( [float( s) for s in file.readline().split()]): coord[ v, j] = c Morally this uglier code with an explicit loop should not be faster but it is with Numarray. With Numeric assignment from a list has reasonable performance. How can it be improved for Numarray? -- Timo Korvola <URL:http://www.iki.fi/tkorvola> ------------------------------------------------------- This SF.Net email is sponsored by: YOU BE THE JUDGE. Be one of 170 Project Admins to receive an Apple iPod Mini FREE for your judgement on who ports your project to Linux PPC the best. Sponsored by IBM. Deadline: Sept. 24. Go here: http://sf.net/ppc_contest.php _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

1 0

PyArray_FromDimsAndData: Numeric interface to PyCObject data
by Berthold Höllmann Sept. 16, 2004

Sept. 16, 2004

Hello, For integrating some codes I try to use PyCObjects with Numeric interfaces. I have PyCObjects that contain several arrays. I want to provide these arrays to Python as Numeric arrays using PyArray_FromDimsAndData, the arrays could grow quite large, so I want to handle arround references, not copies. Of course this is dangerous. If I destroy the PyCObject and try to access the Numeric array referencing its data later, who knows what happens. It would be nice to have some callback function slot to allow to increase the refcount of the PyCObject when a Numeric array with a reference is created and to decrease it when the array is deleted. Is there a way to achive this. Further after skimming over the numarray documentation it seems PyArray_FromDimsAndData and similar functions for numarray are copiing the data instead of using a reference. Is this true? If yes, is it planned to provide a method for generating numarray arrays using references only? Kind regards Berthold Höllmann -- Germanischer Lloyd AG CAE Development Vorsetzen 35 20459 Hamburg Phone: +49(0)40 36149-7374 Fax: +49(0)40 36149-7320 e-mail: hoel(a)gl-group.com Internet: http://www.gl-group.com This e-mail contains confidential information for the exclusive attention of the intended addressee. Any access of third parties to this e-mail is unauthorised. Any use of this e-mail by unintended recipients such as copying, distribution, disclosure etc. is prohibited and may be unlawful. When addressed to our clients the content of this e-mail is subject to the General Terms and Conditions of GL's Group of Companies applicable at the date of this e-mail. GL's Group of Companies does not warrant and/or guarantee that this message at the moment of receipt is authentic, correct and its communication free of errors, interruption etc.

1 0

doc bug in numarray 1.1
by Chris Barker Sept. 15, 2004

Sept. 15, 2004

HI all, I just installed numarray 1.1. All went well with setup.py. Then I decided to try to build it against an atlas lapack. I found, under the Linear Algebra section: """ the setup procedure needs to be modified to force the lapack_lite module to be linked against those rather than the builtin replacement functions. Edit Packages/LinearAlgebra2/setup.py and edit the variables sourcelist, lapack_dirs, and lapack_libs. In sourcelist you should remove all sourcefiles besides .... """ but there is no: Packages/LinearAlgebra2/setup.py However, I did find in: addons.py """ if os.environ.has_key('USE_LAPACK'): BUILTIN_BLAS_LAPACK = 0 else: BUILTIN_BLAS_LAPACK = 1 """ so I tried: export USE_LAPACK=true python setup.py build --gencode Now it appears to be compiling linear_algebra differently, as I now get a linking error, can't find: f90math, fio, or f77math However, if I remove those from lapack_libs in addons.py, I can get it to compile, install, and as far as I can tell, run fine. Why are they in that list? By the way, this is all under Gentoo Linux. -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 Chris.Barker(a)noaa.gov

3 6

character arrays supported by C API?
by Faheem Mitha Sept. 14, 2004

Sept. 14, 2004

Dear People, Are character arrays supported by the Numarray C API? My impression from the documentation is no, but I would appreciate a confirmation. Thanks. Faheem.

1 0

PEP for overiding and/or/not
by Tim Hochberg Sept. 14, 2004

Sept. 14, 2004

In case anyone missed it, Greg Ewing posted a PEP that describes a way to allow the overriding of the and/or/not. Since numeric applications are listed as one of the motivating factors, people here might want to look it over and weight in. There's been some discussion both on python-list and python-dev. -tim

1 0

PyArray_FromDimsAndData and data copying
by esatel Sept. 13, 2004

Sept. 13, 2004

In April of this year (around April 22), there was some talk about altering PyArray_FromDimsAndData work so that it would work without copying data (for compatibility with Numerical). Does anyone know if the change was made? The documentation still suggests the data is copied. Thanks, Eli

1 0

Conference presentations
by Travis E. Oliphant Sept. 13, 2004

Sept. 13, 2004

Hi all, The SciPy 2004 conference was a great success. I personally enjoyed seeing all attendees and finding out about the activity that has been occurring with Python and Science. As promised, all of the presentations that were submitted to abstracts(a)scipy.org are now available on-line under the conference-schedule page. The link is http://www.scipy.org/wikis/scipy04/ConferenceSchedule If anyone who hasn't submitted their presentation would like to, you still can. As I was only able to attend the first day, I cannot comment on the entire conference. However, what I saw was very encouraging. There continues to be a great amount of work being done in using Python for Scientific Computing and the remaining problems seems to be how to get the word out and increase the user base. Many thanks are due to the presenters and the conference sponsors: *The National Biomedical Computation Resource* <http://nbcr.sdsc.edu> (NBCR, UCSD, San Diego, CA) The mission of the National Biomedical Computation Resource at the University of California San Diego and partners at The Scripps Research Institute and Washington University is to conduct, catalyze, and advance biomedical research by harnessing, developing and deploying forefront computational, information, and grid technologies. NBCR is supported by _National Institutes of Health (NIH) _ through a _National Center for Research Resources_ centers grant (P 41 RR08605). *The Center for Advanced Computing Research* <http://www.cacr.caltech.edu/> (CACR, CalTech <http://www.scipy.org/wikis/scipy04/CalTech>, Pasadena, CA) CACR is dedicated to the pursuit of excellence in the field of high-performance computing, communication, and data engineering. Major activities include carrying out large-scale scientific and engineering applications on parallel supercomputers and coordinating collaborative research projects on high-speed network technologies, distributed computing and database methodologies, and related topics. Our goal is to help further the state of the art in scientific computing. *Enthought, Inc.* <http://www.enthought.com> (Austin, TX) Enthought, Inc. provides business and scientific computing solutions through software development, consulting and training. Best regards to all, -Travis Oliphant Brigham Young University 459 CB Provo, UT 84602 oliphant.travis(a)ieee.org

1 0

Reshaing continued
by Karthikesh Raju Sept. 13, 2004

Sept. 13, 2004

Hi All, yes, Reshaping can be done row wise using transpose, for example a = transpose(reshape(arange(0,9),(3,3))) but what about higher dimension arrays. example a = reshape(arange(0,18),(2,3,3)) a[0,:,:], a[1,:,:] should be rows wise extracts like a[0,:,:] = 0 3 6 1 4 7 2 5 8 etc Why cant we define the axis along which reshape should work .. warm regards karthik ----------------------------------------------------------------------- Karthikesh Raju, email: karthik(a)james.hut.fi karthikesh.raju(a)gmail.com Researcher, http://www.cis.hut.fi/karthik Helsinki University of Technology, Tel: +358-9-451 5389 Laboratory of Comp. & Info. Sc., Fax: +358-9-451 3277 Department of Computer Sc., P.O Box 5400, FIN 02015 HUT, Espoo, FINLAND -----------------------------------------------------------------------

4 5

ANN: numarray-1.1
by Todd Miller Sept. 13, 2004

Sept. 13, 2004

Release Notes for numarray-1.1 Numarray is an array processing package designed to efficiently manipulate large multi-dimensional arrays. Numarray is modelled after Numeric and features c-code generated from python template scripts, the capacity to operate directly on arrays in files, and improved type promotions. Although numarray-1.1 is predominantly a bugfix release, if you use numarray, I strongly recommend upgrading. I. ENHANCEMENTS 986194 Add SMP threading Build/install with --smp to enable ufuncs to release the GIL during their compute loops. You have to supply your own threads and partition your array computations among them to realize any SMP benefit. This adds overhead so don't do it unless you have multiple CPUs and know how to manage multiple compute threads. 1016142 CharArray eval() too slow CharArray.fasteval() was modified to use strtod() rather than Python's eval(). This makes it ~70x faster for converting CharArrays to NumArrays. fasteval() no longer works for complex types. eval() still works for everything. 989618 Document memmap.py (memory mapping) 996177 Unsigned int type support limited 1008968 Add kroenecker product II. BUGS FIXED / CLOSED 984286 max.reduce of byteswapped array Sebastian Haase reported that the reduction of large (>100KB) byteswapped arrays did not work correctly. This bug affected reductions and accumulations of byteswapped and misaligned arrays causing them to produce incorrect answers. Thanks Sebastian! 1011456 numeric compatibility byteoffset numarray's Numeric compatibility C-API didn't correctly account for the byte offsets produced by sub-arrays and array slices. This was fixed by re-defining the meaning of the ->data pointer in the PyArrayObject struct to include byteoffset. NA_OFFSETDATA() was likewise redefined to return ->data rather than ->data + ->byteoffset. Correctly written code is still source compatible. Incorrectly written code will generally be transparently fixed. Code which accounted for byteoffset without using NA_OFFSETDATA() will break. This bug affected functions in numarray.numeric as well as add-on packages like numarray.linear_algebra and numarray.fft. 1009462 matrixmultiply (a,b) leaves b transposed Many people reported this side effect. Thanks to all. 919297 Windows build fails VC++ 7.0 964356 random_array.randint exceeds boundaries 985710 buffer not aligned on 8 byte boundary (Windows-98 broken) 990328 Object Array repr for >1000 elements 997898 Invalid sequences errors 1004600 Segfault in array element deletion 1005537 Incorrect handling of overlapping assignments in Numarray 1008375 Weirdness with 'new' method 1008462 searchsorted bug and fix 1009309 randint bug fix patch 1015896 a.is_c_array() mixed int/bool results 1016140 argsort of string arrays See http://sourceforge.net/tracker/?atid=450446&group_id=1369&func=browse for more details. III. CAUTIONS 1. This release is binary incompatible with numarray-1.0. Writers of C-extensions which directly reference the byteoffset field of the PyArrayObject should be aware that the data pointer is now the sum of byteoffset and the buffer base pointer. All C extensions which use the numarray C-API must be recompiled. This incompatibility was an unfortunate consequence of the fix for "numeric compatibility byteoffset". WHERE ----------- Numarray-1.1 windows executable installers, source code, and manual is here: http://sourceforge.net/project/showfiles.php?group_id=1369 Numarray is hosted by Source Forge in the same project which hosts Numeric: http://sourceforge.net/projects/numpy/ The web page for Numarray information is at: http://stsdas.stsci.edu/numarray/index.html Trackers for Numarray Bugs, Feature Requests, Support, and Patches are at the Source Forge project for NumPy at: http://sourceforge.net/tracker/?group_id=1369 REQUIREMENTS ------------------------------ numarray-1.1 requires Python 2.2.2 or greater. AUTHORS, LICENSE ------------------------------ Numarray was written by Perry Greenfield, Rick White, Todd Miller, JC Hsu, Paul Barrett, Phil Hodge at the Space Telescope Science Institute. We'd like to acknowledge the assitance of Francesc Alted, Paul Dubois, Sebastian Haase, Tim Hochberg, Nadav Horesh, Edward C. Jones, Eric Jones, Jochen Kuepper, Travis Oliphant, Pearu Peterson, Peter Verveer, Colin Williams, and everyone else who has contributed with comments and feedback. Numarray is made available under a BSD-style License. See LICENSE.txt in the source distribution for details. -- Todd Miller jmiller(a)stsci.edu

1 0

numarray-->Numeric conversion?
by Francesc Alted Sept. 13, 2004

Sept. 13, 2004

Hi, I've been thinking in ways to convert from/to Numeric to numarray objects in a non-expensive way. For the Numeric --> numarray there is an very easy way to do that: In [45]: num=Numeric.arange(10, typecode="i") In [46]: na=numarray.array(buffer(num), typecode=num.typecode(), shape=num.shape) In [47]: na Out[47]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) that creates a new numarray object without a memory copy In [48]: num[2]=3 In [49]: num Out[49]: array([0, 1, 3, 3, 4, 5, 6, 7, 8, 9],'i') In [50]: na Out[50]: array([0, 1, 3, 3, 4, 5, 6, 7, 8, 9]) i.e. both num (Numeric) and na (numarray) arrays shares the same memory. If you delete one reference: In [51]: del num the other is still accessible In [52]: na Out[52]: array([0, 1, 3, 3, 4, 5, 6, 7, 8, 9]) However, it seems that it is not so easy to go the other way, that is convert a numarray object to a Numeric without a memory copy. It seems that Numeric.array constructor get the shape from the sequence object that is passed, while numarray approach seems more sofisticated in the sense that if it detects that the first argument is a buffer sequence, you must specify the shape on the constructor. Anyone knows if building a Numeric from a buffer (specifying both type and shape) is possible (I mean, at Python level) at all?. If that would be the case, adapting libraries that use Numeric to numarray objects and vice-versa would be very easy and cheap (in terms of memory access). -- Francesc Alted

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extracting a random subset of a vector
by Curzio Basso Sept. 8, 2004

Sept. 8, 2004

Hi all, I have an optimization problem. I currently use the following code to select a random subset of a rank-1 array: ---------------------------------------------------------- import numarray as NA import numarray.random_array as RA N = 1000 M = 100 full = NA.arange(N) subset = full[RA.permutation(N)][:M] --------------------------------------------------------- However, it's quite slow (at least with N~40k), and from the hotshot output is looks like it's the indexing, not the permutation, which takes time. Does anyone have a suggestion on a faster pure-python implementation? thanks

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Compare strings.array with None.
by Shin Sept. 7, 2004

Sept. 7, 2004

I got the following strange behavior in numarray.strings. Is it intended or a bug? Thanks. >>> from numarray import * >>> x = [1,2] >>> x == None 0 >>> from numarray import strings >>> x = strings.array(['a','b']) >>> x == None TypeError (And exit from mainloop.) -- Daehyok Shin

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possible bug with character arrays and shuffle
by Faheem Mitha Sept. 5, 2004

Sept. 5, 2004

Hi, Consider ******************************************************************** In [1]: import random In [2]: import numarray.strings as numstr In [3]: foo = numstr.array(['a', 'c', 'a', 'g', 'g', 'g', 'g', ...: 'g','a','a','a','a'],shape=(12,1)) In [4]: foo Out[4]: CharArray([['a'], ['c'], ['a'], ['g'], ['g'], ['g'], ['g'], ['g'], ['a'], ['a'], ['a'], ['a']]) In [5]: for i in range(50): ...: random.shuffle(foo) ...: In [6]: foo Out[6]: CharArray([['a'], ['a'], ['a'], ['a'], ['a'], ['a'], ['a'], ['a'], ['a'], ['a'], ['a'], ['a']]) ****************************************************************** Either I am doing something horribly wrong, or shuffle is badly broken, since it is supposed to "shuffle list x in place". I haven't checked shuffle carefully, but it seems to randomize for a bit Ok, and then after a while, it suddenly starts returning all the same characters. It took me a while to track this one down. This is with Debian sarge and package versions ii python2.3-numarray 1.0-2 An array processing package modelled after Python-Numeric ii python 2.3.4-1 An interactive high-level object-oriented language (default version) Please advise of a possible workaround. Or should I simply use a different randomizing function, like sample? Please cc me, I'm not subscribed. Thanks. Faheem.

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possible bug in concatenating character arrays
by Faheem Mitha Sept. 5, 2004

Sept. 5, 2004

The following recipe gives a segmentation fault. ************************************************************************ In [1]: import numarray.strings as numstr In [2]: foo = numstr.array("acgttatcgt", shape=(3,3)) In [3]: foo[0] + foo[1] Segmentation fault ************************************************************************* It works if instead one does: ************************************************************************* In [4]: a = foo[0] In [5]: b = foo[1] In [6]: a Out[6]: CharArray(['a', 'c', 'g']) In [7]: b Out[7]: CharArray(['t', 't', 'a']) In [8]: a + b Out[8]: CharArray(['at', 'ct', 'ga']) *************************************************************************** In the case of numerical arrays, either method works. In [9]: bar = numarray.array(range(9), shape=(3,3)) In [10]: bar Out[10]: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) In [11]: a = bar[0] In [12]: b = bar[1] In [13]: a + b Out[13]: array([3, 5, 7]) In [14]: bar[0] + bar[1] Out[14]: array([3, 5, 7]) ************************************************************ If this is a bug (I'd appreciate confirmation of this), should I report to the bug tracking system? Please cc me, I'm not subscribed. Faheem.

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RE: [Numpy-discussion] possible bug in concatenating character arrays
by Nadav Horesh Sept. 4, 2004

Sept. 4, 2004

It works without any problem under numarray 1.1 (from CVS) and python 2.3.4 and 2.4.a2 under RH9 Nadav. -----Original Message----- From: Faheem Mitha [mailto:faheem@email.unc.edu] Sent: Sun 05-Sep-04 04:45 To: numpy-discussion(a)lists.sourceforge.net Cc: Subject: [Numpy-discussion] possible bug in concatenating character arrays The following recipe gives a segmentation fault. ************************************************************************ In [1]: import numarray.strings as numstr In [2]: foo = numstr.array("acgttatcgt", shape=(3,3)) In [3]: foo[0] + foo[1] Segmentation fault ************************************************************************* It works if instead one does: ************************************************************************* In [4]: a = foo[0] In [5]: b = foo[1] In [6]: a Out[6]: CharArray(['a', 'c', 'g']) In [7]: b Out[7]: CharArray(['t', 't', 'a']) In [8]: a + b Out[8]: CharArray(['at', 'ct', 'ga']) *************************************************************************** In the case of numerical arrays, either method works. In [9]: bar = numarray.array(range(9), shape=(3,3)) In [10]: bar Out[10]: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) In [11]: a = bar[0] In [12]: b = bar[1] In [13]: a + b Out[13]: array([3, 5, 7]) In [14]: bar[0] + bar[1] Out[14]: array([3, 5, 7]) ************************************************************ If this is a bug (I'd appreciate confirmation of this), should I report to the bug tracking system? Please cc me, I'm not subscribed. Faheem. ------------------------------------------------------- This SF.Net email is sponsored by BEA Weblogic Workshop FREE Java Enterprise J2EE developer tools! Get your free copy of BEA WebLogic Workshop 8.1 today. http://ads.osdn.com/?ad_id=5047&alloc_id=10808&op=click _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

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RE: [Numpy-discussion] possible bug in concatenating character arrays
by Nadav Horesh Sept. 4, 2004

Sept. 4, 2004

I dont get this result with numarray 1.1 (from CVS) with python versions 2.3.4 and 2.4.a2: >>> import numarray.strings as numstr >>> foo = numstr.array("acgttatcgt", shape=(3,3)) >>> foo[0] + foo[1] CharArray(['at', 'ct', 'ga']) Nadav. -----Original Message----- From: Faheem Mitha [mailto:faheem@email.unc.edu] Sent: Sun 05-Sep-04 04:45 To: numpy-discussion(a)lists.sourceforge.net Cc: Subject: [Numpy-discussion] possible bug in concatenating character arrays The following recipe gives a segmentation fault. ************************************************************************ In [1]: import numarray.strings as numstr In [2]: foo = numstr.array("acgttatcgt", shape=(3,3)) In [3]: foo[0] + foo[1] Segmentation fault ************************************************************************* It works if instead one does: ************************************************************************* In [4]: a = foo[0] In [5]: b = foo[1] In [6]: a Out[6]: CharArray(['a', 'c', 'g']) In [7]: b Out[7]: CharArray(['t', 't', 'a']) In [8]: a + b Out[8]: CharArray(['at', 'ct', 'ga']) *************************************************************************** In the case of numerical arrays, either method works. In [9]: bar = numarray.array(range(9), shape=(3,3)) In [10]: bar Out[10]: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) In [11]: a = bar[0] In [12]: b = bar[1] In [13]: a + b Out[13]: array([3, 5, 7]) In [14]: bar[0] + bar[1] Out[14]: array([3, 5, 7]) ************************************************************ If this is a bug (I'd appreciate confirmation of this), should I report to the bug tracking system? Please cc me, I'm not subscribed. Faheem. ------------------------------------------------------- This SF.Net email is sponsored by BEA Weblogic Workshop FREE Java Enterprise J2EE developer tools! Get your free copy of BEA WebLogic Workshop 8.1 today. http://ads.osdn.com/?ad_id=5047&alloc_id=10808&op=click _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

1 0

Reshaping along an axis
by Karthikesh Raju Sept. 3, 2004

Sept. 3, 2004

Hi all, Suppose i have a tuple or a 1D array as a = (1,2,3,4,5,6,7,8,9) presently reshape(a,(3,3)) gives me 1 2 3 4 5 6 7 8 9 i.e reshaping is done column wise. How do one specifiy reshape to work row wise as: reshape(a,(3,3)) = 1 4 7 2 5 8 3 6 9 With warm regards karthik ----------------------------------------------------------------------- Karthikesh Raju, email: karthik(a)james.hut.fi karthikesh.raju(a)gmail.com Researcher, http://www.cis.hut.fi/karthik Helsinki University of Technology, Tel: +358-9-451 5389 Laboratory of Comp. & Info. Sc., Fax: +358-9-451 3277 Department of Computer Sc., P.O Box 5400, FIN 02015 HUT, Espoo, FINLAND -----------------------------------------------------------------------

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efficient summation
by Darren Dale Sept. 1, 2004

Sept. 1, 2004

I am trying to effieciently sum over a subset of the elements of a matrix. In Matlab, this could be done like: a=[1,2,3,4,5,6,7,8,9,10] b = [1,0,0,0,0,0,0,0,0,1] res=sum(a(b)) %this sums the elements of a which have corresponding elements in b that are true Is there anything similar in numarray (or numeric)? I thought masked arrays looked promising, but I find that masking 90% of the elements results in marginal speedups (~5%, instead of 90%) over the unmasked array. Thanks! Darren

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Re: [Numpy-discussion] sum of a masked array
by smpitts＠ou.edu Sept. 1, 2004

Sept. 1, 2004

Darren, I tried your code on my system: Python 2.2 and numarray 1.0. The type error looks like a bug in the array display code. >>> Rx = ones((2500,2500)) >>> N = make_mask_none((2500,2500)) >>> Rx = array(Rx,mask=N) >>> print average(Rx) ## works [ 1. 1. 1. ..., 1. 1. 1.] >>> s = sum(Rx) >>> print s Traceback (most recent call last): File "<stdin>", line 1, in ? File "/usr/lib/python2.2/site-packages/numarray/ma/MA.py", line 742, in __str__ return str(filled(self, f)) File "/usr/lib/python2.2/site-packages/numarray/generic.py", line 499, in __str__ return arrayprint.array2string(self, separator=" ", style=str) File "/usr/lib/python2.2/site-packages/numarray/arrayprint.py", line 188, in array2string separator, prefix) File "/usr/lib/python2.2/site-packages/numarray/arrayprint.py", line 137, in _array2string data = _leading_trailing(a) File "/usr/lib/python2.2/site-packages/numarray/arrayprint.py", line 105, in _leading_trailing b = _gen.concatenate((a[:_summaryEdgeItems], File "/usr/lib/python2.2/site-packages/numarray/generic.py", line 1028, in concatenate return _concat(arrs) File "/usr/lib/python2.2/site-packages/numarray/generic.py", line 1012, in _concat dest = arrs[0].__class__(shape=destShape, type=convType) TypeError: __init__() got an unexpected keyword argument 'type' but the array is fine >>> for i in range(2500): ... assert s[i] == 2500 >>> Hopefully someone with more knowledge can help you out. I still use MA with Numeric for the most part. -- Stephen Pitts smpitts(a)ou.edu

2 1

sum of a masked array
by Darren Dale Sept. 1, 2004

Sept. 1, 2004

Hi, I'm new to the list. I am having some difficulty finding the sum of a masked array. I'm using numarray 1.0 installed on winXP with Python 2.3.4. from numarray.ma import * Rx = ones((2500,2500)) N = make_mask_none((2500,2500)) Rx = array(Rx,mask=N) print average(Rx) ## works print sum(Rx) ## gives an error message 16 lines long, the most recent being a type error. Is there something obvious I am doing wrong here?

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