Pierre Thibault wrote: > Hum, this example seems like a special case not really appropriate for my > needs. Let me make my problem a little more precise. The objects I'll want > to iterate through will always contain some floats. Very often, I guess > they will be numpy.ndarray instances, but they can be something else as > well. For instance, I will have to deal with arrays having internal > symmetries (crystallographic datasets). The most efficient thing to do > with those is save only the unique values and keep track of the symmetry > operations needed to extract other values. > > A 1d examples: I have a class which represents a unit cell > which has the additional mirror symmetry in the middle of the cell. Say > C is an instance of this class representing data on a 50-long array. Only > 25 datum will be stored in C, and the class takes care of giving the value > of C[0] if C[49] is asked, C[1] for C[48], and so on. Since crystals are > periodic, the translational symmetry can also be managed (C[-1] == C[49] > == C[0], C[-2] == C[48] == C[1]...). In any case, the user of this object > need not know how the data is stored: for him, C is just a funny > array-like object defined from -infinity to +infinity on a 1-D lattice. > > Now, I want to do simple math operations on the data in C. Doing a loop > from 0 to 49 would loop twice through the actual data. In this > context, an iterator is perfect since it can take care internally of going > through the data only once, so it would be great to access _AND MODIFY_ > data over which I iterate.
That would not only be nice but crucial to the integrity of your data. You'd have to explicitly forbid operations on individual cells as applying an operation to a single cell would change two or more cells, depending of the kind of symmetry. > I now realize I don't know how to do that with numpy.ndarray either. It > looks like the only way I can modify the content of an array is by using > the [] notation (that is, in random access). For instance, the > following will not change the state of a: > > import numpy > a = numpy.array([[1.,2.,3.],[4.,5.,6.],[7.,8.,9.]]) > > for c in a.flat > c += 2. > > Of course, I know that a += .2 would be alright, but this is not general > enough for more complicated classes. > > So I guess my problem is worst than I thought. Indeed, but I don't think it would be easier in C++... Peter -- http://mail.python.org/mailman/listinfo/python-list
