Neil Martinsen-Burrell wrote: > On 06/16/2009 02:18 PM, Robert wrote: >> >>> n = 10 >> >>> xx = np.ones(n) >> >>> yy = np.arange(n) >> >>> aa = np.column_stack((xx,yy)) >> >>> bb = np.column_stack((xx+1,yy)) >> >>> aa >> array([[ 1., 0.], >> [ 1., 1.], >> [ 1., 2.], >> [ 1., 3.], >> [ 1., 4.], >> [ 1., 5.], >> [ 1., 6.], >> [ 1., 7.], >> [ 1., 8.], >> [ 1., 9.]]) >> >>> bb >> array([[ 2., 0.], >> [ 2., 1.], >> [ 2., 2.], >> [ 2., 3.], >> [ 2., 4.], >> [ 2., 5.], >> [ 2., 6.], >> [ 2., 7.], >> [ 2., 8.], >> [ 2., 9.]]) >> >>> np.column_stack((aa,bb)) >> array([[ 1., 0., 2., 0.], >> [ 1., 1., 2., 1.], >> [ 1., 2., 2., 2.], >> [ 1., 3., 2., 3.], >> [ 1., 4., 2., 4.], >> [ 1., 5., 2., 5.], >> [ 1., 6., 2., 6.], >> [ 1., 7., 2., 7.], >> [ 1., 8., 2., 8.], >> [ 1., 9., 2., 9.]]) >> >>> cc = _ >> >>> cc.reshape((n*2,2)) >> array([[ 1., 0.], >> [ 2., 0.], >> [ 1., 1.], >> [ 2., 1.], >> [ 1., 2.], >> [ 2., 2.], >> [ 1., 3.], >> [ 2., 3.], >> [ 1., 4.], >> [ 2., 4.], >> [ 1., 5.], >> [ 2., 5.], >> [ 1., 6.], >> [ 2., 6.], >> [ 1., 7.], >> [ 2., 7.], >> [ 1., 8.], >> [ 2., 8.], >> [ 1., 9.], >> [ 2., 9.]]) >> >>> >> >> >> However I feel too, there is a intuitive abbrev function like >> 'interleave' or so missing in numpy shape_base or so. > > Using fancy indexing, you can set strided portions of an array equal to > another array. So:: > > In [2]: aa = np.empty((10,2)) > > In [3]: aa[:, 0] = 1 > > In [4]: aa[:,1] = np.arange(10) > > In [5]: bb = np.empty((10,2)) > > In [6]: bb[:,0] = 2 > > In [7]: bb[:,1] = aa[:,1] # this works > > In [8]: cc = np.empty((20,2)) > > In [9]: cc[::2,:] = aa > > In [10]: cc[1::2,:] = bb > > In [11]: cc > Out[11]: > array([[ 1., 0.], > [ 2., 0.], > [ 1., 1.], > [ 2., 1.], > [ 1., 2.], > [ 2., 2.], > [ 1., 3.], > [ 2., 3.], > [ 1., 4.], > [ 2., 4.], > [ 1., 5.], > [ 2., 5.], > [ 1., 6.], > [ 2., 6.], > [ 1., 7.], > [ 2., 7.], > [ 1., 8.], > [ 2., 8.], > [ 1., 9.], > [ 2., 9.]]) > > Using this syntax, interleave could be a one-liner. > > -Neil
that method of 'filling an empty with a pattern' was mentioned in the other (general) interleaving question. It requires however a lot of particular numbers and :'s in the code, and requires even more statements which can hardly be written in functional style - in one line?. The other approach is more jount, free of fancy indexing assignments. The general interleaving should work efficiently in one like this: np.column_stack/concatenate((r,g,b,....), axis=...).reshape(..) But as all this is not intuitive, something like this should be in numpy perhaps? : def interleave( tup_arrays, axis = None ) Robert _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
