[Numpy-discussion] question about optimizing

Anne Archibald peridot.faceted@gmail....
Sat May 17 15:54:03 CDT 2008

2008/5/17 Anne Archibald <peridot.faceted@gmail.com>:
> 2008/5/17 Brian Blais <bblais@bryant.edu>:
>> 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:


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