On Wed, 26 Jul 2006 20:59:12 +0200, Peter Otten wrote: > 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
Well, no, C++ is better in this case because the thing you iterate over is a generalized pointer, which can be dereferenced to get access to the memory. So in other word, iterator.next() (in python) is a copy, while *iterator (in C++) is a reference. Thanks anyway for your comments! Pierre -- http://mail.python.org/mailman/listinfo/python-list
