I've found that there are two ways to represent an array of 3D points: # 01:
N = 4 a = numpy.zeros((N, 3), dtype='f8') # 02: N = 4 b = numpy.zeros(N, dtype='3f8') The second representation seems "conceptually" better, it is really an 1D array of N elements ( i.e., b.shape == (4,) ), but, "in practice", it is not very useful, because I can't directly add it to scalar, or to another array of points, and I can't multiply it by a transformation matrix. With the first representation, I can use expressions like this: c = (a+1) * numpy.matrix([[1,0,0],[0,0,3],[0,2,0]]) - numpy.array([1, 5.5, 0]) So, I chose the second representation for my application; the problem is that its shape is (N, 3), and I haven't found a way to distinguish it from a Nx3 array of scalars, for example. This distinction is needed, because I must convert it to other array types (from other libraries), I must make proper I/O of the array, etc. Also, my application uses 2D and 3D arrays, and they can be arrays of scalars, of points or of small matrices... so, for example, a 10x7 array of 2x2 matrices of double is represented in numpy as a 10x7x2x2 array of f8 (without the proper information, it could be viewed also as a 10x7x2 array of 2D points or a 10x7x2x2 array of doubles, and I DON'T want this). My question is: * Is there any field in the NumPy object where I can keep this information (the shape of the "element"), without creeping it with the dtype='(M,N)f8' representation I explained above? Thanks, Edson
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