2011/11/23 <josef.p...@gmail.com>: > might be an old story >>> np.__version__ -> '1.5.1'
I have only a numpy 1.4.1 for testing atm, but I believe this corner case never occured and hence never got fixed since then. Confirming your issue on that. > It thought for once it's easier to use reshape to add a new axis > instead of ...,None > but my results got weird (normal(0,1) sample of 2.13795875e-314) What do you mean by "sample of 2.13....e-314"? >>>> x = 1 >>>> y = np.arange(3) >>>> z = np.arange(2)[:,None] >>>> np.broadcast(x,y,z) > <numpy.broadcast object at 0x04C0DCA0> >>>> np.broadcast_arrays(x,y,z) > [array([[1, 1, 1], > [1, 1, 1]]), array([[0, 1, 2], > [0, 1, 2]]), array([[0, 0, 0], > [1, 1, 1]])] >>>> x1, y1, z1 = np.broadcast_arrays(x,y,z) >>>> map(np.shape, (x1, y1, z1)) > [(2, 3), (2, 3), (2, 3)] > > shape looks fine, let's add an extra axis with reshape > >>>> x1.reshape(2,3,1) > array([[[ 1], > [ 1], > [ 1099464714]], > > [[-2147481592], > [ 184], > [ 1]]]) > > what's that ? I think it has to do with this: >>> x1.strides (0, 0) Let's have a look at http://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html#numpy.reshape. From there we have this nice examples: " >>> a = np.array([[1,2,3], [4,5,6]]) >>> np.reshape(a, 6) array([1, 2, 3, 4, 5, 6]) >>> np.reshape(a, 6, order='F') array([1, 4, 2, 5, 3, 6]) " So my guess would be that numpy.reshape either assumes C or F order, I don't know what the "preserve" 'A' flag has to be interpreted as? At least this would explain the behaviour. >>>> (0+x1).reshape(2,3,1) > array([[[1], > [1], > [1]], > > [[1], > [1], > [1]]]) > >>>> (y1*1.).reshape(2,3,1) > array([[[ 0.], > [ 1.], > [ 2.]], > > [[ 0.], > [ 1.], > [ 2.]]]) > >>>> (y1).reshape(2,3,1) > array([[[ 0], > [ 1], > [ 2]], > > [[ 0], > [ 1099447643], > [-2147483648]]]) Guess the zero in the second block of (y1) is just coincidentally. >>>> x1, y1, z1 = np.broadcast_arrays(x,y,z) >>>> x1[...,None] > array([[[1], > [1], > [1]], > > [[1], > [1], > [1]]]) > >>>> x1.shape > (2, 3) >>>> x1.reshape(2,3,1) > array([[[ 1], > [ 1], > [ 1099464730]], > > [[-2147479536], > [ -445054780], > [ 1063686842]]]) > > > the background story: playing broadcasting tricks for > http://projects.scipy.org/scipy/ticket/1544 Have not looked at that. > Josef Some more: >>> x1.reshape((2, 3, 1), order='A') array([[[ 1], [2506242], [6755216]], [[ 262144], [7272256], [7278624]]]) >>> x1.reshape((2, 3, 1), order='C') array([[[ 1], [2506242], [6755216]], [[ 262144], [7272256], [7278624]]]) >>> x1.reshape((2, 3, 1), order='F') array([[[ 1], [6755216], [7272256]], [[2506242], [ 262144], [7278624]]]) So I guess it's not a bug, it's a feature ;-) Reshape is low-level plumbing apparently. It should be, since the strides involved not necessarily are compatible with each other, you can reshape (11, 7) to (7, 11). Maybe a warning if the strides contain zeros might be appropriate to avoid reading in arbitrary memory, though. Friedrich _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
