>>> aa=matrix([[-1, 2, 0],[0, 0, 3]]) >>> aa matrix([[-1, 2, 0], [ 0, 0, 3]]) >>> aa.nonzero() (matrix([[0, 0, 1]], dtype=int64), matrix([[0, 1, 2]], dtype=int64)) *********OK********* >>> npy.nonzero(aa.flat) (array([0, 1, 5], dtype=int64),) *********OK********* >>> flatnonzero(aa) matrix([[0, 0, 0]], dtype=int64) *******This is Wrong********** If I convert aa to an ndarray, it is OK then aaa=asarray(aa) >>> flatnonzero(aaa) array([0, 1, 5], dtype=int64)
Then I figure it out that it might be induced by the behavior of ravel() >>> aaa.shape (2L, 3L) >>> aaa.ravel().shape (6L,) >>> aa.ravel() matrix([[-1, 2, 0, 0, 0, 3]]) >>> _.shape (1L, 6L) Why not make ravel() behaviors consistent under both ndarray and matrix contexts? Or make different flatnonzero() for the matrix context? m.ravel().nonzero()[1]# for matrix a.ravel().nonzero()[0]# for ndarray _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
