I see my slicing was the problem, np.vstack((test[:1], test)) works perfectly.
On Wed, Oct 27, 2010 at 12:55 AM, <josef.p...@gmail.com> wrote: > On Tue, Oct 26, 2010 at 8:15 PM, Dewald Pieterse > <dewald.piete...@gmail.com> wrote: > > Starting with: > > > >> In [93]: test = > >> numpy.array([[[1,1,1],[1,1,1]],[[2,2,2],[2,2,2]],[[3,3,3],[3,3,3]]]) > >> > >> In [94]: test > >> Out[94]: > >> array([[[1, 1, 1], > >> [1, 1, 1]], > >> > >> [[2, 2, 2], > >> [2, 2, 2]], > >> > >> [[3, 3, 3], > >> [3, 3, 3]]]) > >> > >> Slicing the complete first row: > >> > >> In [95]: firstrow = test[0,:,:] > >> > >> In [96]: firstrow > >> Out[96]: > >> array([[1, 1, 1], > >> [1, 1, 1]]) > > > > I want to stack firstrow onto test to end up with: > > > >> ([[[1, 1, 1], > >> [1, 1, 1]], > >> > >> [[1, 1, 1], > >> [1, 1, 1]], > >> > >> [[2, 2, 2], > >> [2, 2, 2]], > >> > >> [[3, 3, 3], > >> [3, 3, 3]]] > > > > > > vstack wants the array dimensions to be the same, is this possible > without > > doing 1 dimensional reshape, the actual data I want to do this on is some > > what larger. > > > >> numpy.vstack((firstrow,test)) > >> > >> > --------------------------------------------------------------------------- > >> ValueError Traceback (most recent call > >> last) > >> > >> /mnt/home/home/bmeagle/M/programme/analiseerverwerkteprent.py in > >> <module>() > >> ----> 1 > >> 2 > >> 3 > >> 4 > >> 5 > >> > >> /usr/lib64/python2.6/site-packages/numpy/core/shape_base.py in > vstack(tup) > >> 212 > >> 213 """ > >> --> 214 return _nx.concatenate(map(atleast_2d,tup),0) > >> 215 > >> 216 def hstack(tup): > >> > >> ValueError: arrays must have same number of dimensions > > > > > > What is the correct python way to do this? > > keep the first dimension or add it back in > > >>> test = > np.array([[[1,1,1],[1,1,1]],[[2,2,2],[2,2,2]],[[3,3,3],[3,3,3]]]) > >>> np.vstack((test[:1], test)) > array([[[1, 1, 1], > [1, 1, 1]], > > [[1, 1, 1], > [1, 1, 1]], > > [[2, 2, 2], > [2, 2, 2]], > > [[3, 3, 3], > [3, 3, 3]]]) > >>> np.vstack((test[0][None,...], test)) > array([[[1, 1, 1], > [1, 1, 1]], > > [[1, 1, 1], > [1, 1, 1]], > > [[2, 2, 2], > [2, 2, 2]], > > [[3, 3, 3], > [3, 3, 3]]]) > >>> np.vstack((test[0][None,:,:], test)) > array([[[1, 1, 1], > [1, 1, 1]], > > [[1, 1, 1], > [1, 1, 1]], > > [[2, 2, 2], > [2, 2, 2]], > > [[3, 3, 3], > [3, 3, 3]]]) > > I like expand_dims for arbitrary axis, e.g. > > >>> ax=1 > >>> np.concatenate((np.expand_dims(test[:,0,:],ax), test), ax) > array([[[1, 1, 1], > [1, 1, 1], > [1, 1, 1]], > > [[2, 2, 2], > [2, 2, 2], > [2, 2, 2]], > > [[3, 3, 3], > [3, 3, 3], > [3, 3, 3]]]) > > Josef > > > > > > > > -- > > Dewald Pieterse > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Dewald Pieterse "A democracy is nothing more than mob rule, where fifty-one percent of the people take away the rights of the other forty-nine." ~ Thomas Jefferson
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