Hi, I have an NxM array, which I am indexing with a 1-d, length N boolean array. For example, with a 3x5 array:
In [1]: import numpy In [2]: data = numpy.arange(15) In [3]: data.shape = 3, 5 Now, I want to select rows 0 and 2, so I can do: In [4]: mask = numpy.array([True, False, True]) In [5]: data[mask] Out[5]: array([[ 0, 1, 2, 3, 4], [10, 11, 12, 13, 14]]) But when the shape of 'data' is a 0xM, this indexing fails: In [6]: data2 = numpy.zeros((0, 5), 'd') In [7]: mask2 = numpy.zeros(0, 'bool') In [8]: data2[mask2] ------------------------------------------------------------ Traceback (most recent call last): File "<ipython console>", line 1, in <module> IndexError: invalid index I would have expected the above to give me a 0x5 array. Of course, I can check on "len(data)" and not use the above indexing when it is zero, but I am hoping that I don't need to special case the boundary condition and have numpy fancy indexing do the "right thing" always. Is this a bug in numpy? Is there any other way to do what I am doing? Here is my numpy setup (numpy installed from the git repository): In [1]: import numpy In [2]: numpy.__version__ Out[2]: '1.6.0.dev-13c83fd' In [3]: numpy.show_config() blas_info: libraries = ['blas'] library_dirs = ['/usr/lib'] language = f77 lapack_info: libraries = ['lapack'] library_dirs = ['/usr/lib'] language = f77 atlas_threads_info: NOT AVAILABLE blas_opt_info: libraries = ['blas'] library_dirs = ['/usr/lib'] language = f77 define_macros = [('NO_ATLAS_INFO', 1)] atlas_blas_threads_info: NOT AVAILABLE lapack_opt_info: libraries = ['lapack', 'blas'] library_dirs = ['/usr/lib'] language = f77 define_macros = [('NO_ATLAS_INFO', 1)] atlas_info: NOT AVAILABLE lapack_mkl_info: NOT AVAILABLE blas_mkl_info: NOT AVAILABLE atlas_blas_info: NOT AVAILABLE mkl_info: NOT AVAILABLE In [4]: import sys In [5]: print sys.version 2.6.5 (r265:79063, Apr 16 2010, 13:09:56) [GCC 4.4.3] Thanks! _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion