It is not as bad as I thought, but there is certainly room for improvement.
File `numpy/core/src/multiarraymodule.c' Lines executed:63.56% of 3290 File `numpy/core/src/arrayobject.c' Lines executed:59.70% of 5280 File `numpy/core/src/scalartypes.inc.src' Lines executed:31.67% of 963 File `numpy/core/src/arraytypes.inc.src' Lines executed:47.35% of 868 File `numpy/core/src/arraymethods.c' Lines executed:57.65% of 739 On 6/30/06, Sasha <[EMAIL PROTECTED]> wrote: > As soon as I sent out my 10% estimate, I realized that someone will > challenge it with a python level coverage statistics. My main concern > is not what fraction of numpy functions is called by unit tests, but > what fraction of special cases in the C code is exercised. I am not > sure that David's statistics even answers the first question - I would > guess it only counts statements in the pure python methods and > ignores methods implemented in C. > > Can someone post C-level statistics from gcov > <http://gcc.gnu.org/onlinedocs/gcc/Gcov.html> or a similar tool? > > On 6/30/06, David M. Cooke <[EMAIL PROTECTED]> wrote: > > On Fri, 30 Jun 2006 12:35:35 -0400 > > Sasha <[EMAIL PROTECTED]> wrote: > > > > > On 6/30/06, Fernando Perez <[EMAIL PROTECTED]> wrote: > > > > ... > > > > Besides, decent unit tests will catch these problems. We all know > > > > that every scientific code in existence is unit tested to the smallest > > > > routine, so this shouldn't be a problem for anyone. > > > > > > Is this a joke? Did anyone ever measured the coverage of numpy > > > unittests? I would be surprized if it was more than 10%. > > > > A very quick application of the coverage module, available at > > http://www.garethrees.org/2001/12/04/python-coverage/ > > gives me 41%: > > > > Name Stmts Exec Cover > > --------------------------------------------------- > > numpy 25 20 80% > > numpy._import_tools 235 175 74% > > numpy.add_newdocs 2 2 100% > > numpy.core 28 26 92% > > numpy.core.__svn_version__ 1 1 100% > > numpy.core._internal 99 48 48% > > numpy.core.arrayprint 251 92 36% > > numpy.core.defchararray 221 58 26% > > numpy.core.defmatrix 259 186 71% > > numpy.core.fromnumeric 319 153 47% > > numpy.core.info 3 3 100% > > numpy.core.ma 1612 1145 71% > > numpy.core.memmap 64 14 21% > > numpy.core.numeric 323 138 42% > > numpy.core.numerictypes 236 204 86% > > numpy.core.records 272 32 11% > > numpy.dft 6 4 66% > > numpy.dft.fftpack 128 31 24% > > numpy.dft.helper 35 32 91% > > numpy.dft.info 3 3 100% > > numpy.distutils 13 9 69% > > numpy.distutils.__version__ 4 4 100% > > numpy.distutils.ccompiler 296 49 16% > > numpy.distutils.exec_command 409 27 6% > > numpy.distutils.info 2 2 100% > > numpy.distutils.log 37 18 48% > > numpy.distutils.misc_util 945 174 18% > > numpy.distutils.unixccompiler 34 11 32% > > numpy.dual 41 27 65% > > numpy.f2py.info 2 2 100% > > numpy.lib 30 28 93% > > numpy.lib.arraysetops 121 59 48% > > numpy.lib.function_base 501 70 13% > > numpy.lib.getlimits 76 61 80% > > numpy.lib.index_tricks 223 56 25% > > numpy.lib.info 4 4 100% > > numpy.lib.machar 174 154 88% > > numpy.lib.polynomial 357 52 14% > > numpy.lib.scimath 51 19 37% > > numpy.lib.shape_base 220 24 10% > > numpy.lib.twodim_base 77 51 66% > > numpy.lib.type_check 110 75 68% > > numpy.lib.ufunclike 37 24 64% > > numpy.lib.utils 42 23 54% > > numpy.linalg 5 3 60% > > numpy.linalg.info 2 2 100% > > numpy.linalg.linalg 440 71 16% > > numpy.random 10 6 60% > > numpy.random.info 4 4 100% > > numpy.testing 3 3 100% > > numpy.testing.info 2 2 100% > > numpy.testing.numpytest 430 214 49% > > numpy.testing.utils 151 62 41% > > numpy.version 7 7 100% > > --------------------------------------------------- > > TOTAL 8982 3764 41% > > > > (I filtered out all the *.tests.* modules). Note that you have to import > > numpy after starting the coverage, because we use a lot of module-level code > > that wouldn't be caught otherwise. > > > > -- > > |>|\/|< > > /--------------------------------------------------------------------------\ > > |David M. Cooke http://arbutus.physics.mcmaster.ca/dmc/ > > |[EMAIL PROTECTED] > > > Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
