2008/5/17 Anne Archibald <[EMAIL PROTECTED]>: > 2008/5/17 Brian Blais <[EMAIL PROTECTED]>: > >> at least for me, that was the motivation. I am trying to build a simulation >> framework for part of the brain, which requires connected layers of nodes. >> A layer is either a 1D or 2D structure of nodes, with each node a >> relatively complex beast. Rather than reinvent the indexing (1D, 2D, >> slicing, etc...), I just inherited from ndarray. I thought, after the fact, >> that some numpy functions on arrays would help speed up the code, which >> consists mostly of calling an update function on all nodes, passing each >> them an input vector. I wasn't sure if there would be any speed up for >> this, compared to >> for n in self.flat: >> n.update(input_vector) >> From the response, the answer seems to be no, and that I should stick with >> the python loops for clarity. But also, the words of Anne Archibald, makes >> me think that I have made a bad choice by inheriting from ndarray, although >> I am not sure what a convenient alternative would be. > > Well, it doesn't exist yet, but a handy tool would be a factory > function "ArrayOf"; you would pass it a class, and it would produce a > subclass of ndarray designed to contain that class. That is, the > underlying storage would be a record array, but the getitem and > setitem would automatically handle conversion to and from the class > you supplied it, where appropriate. > > myarray = ArrayOf(Node,dtype=...) > A = myarray.array([Node(...), Node(...), Node(...)]) > n = A[1] > A[2] = Node(...) > A.C.update() # python-loop-based update of all elements > > You could also design it so that it was easy to derive a class from > it, since that's probably the best way to handle vectorized methods: > > class myarray(ArrayOf(Node, dtype=...)): > def update(self): > self.underlying["node_attribute"] += 1
So just as an experiment I implemented some of this: http://www.scipy.org/Cookbook/Obarray Anne _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
