Hi,
I'm writing a Python program using combinatorial algorithms to generate music.
It originally was of this general form:
First, a combinatorial function producing a list of sub-lists, then:
For each sub-list in the list:
Filter/modifier A, then append modified sub-list to new list,
For each sublist in new list:
Filter/modifier B, then append modified sub-list to new list,
......and so on for C, D,etc.
Finally, for each sub-list in the final list, output the sub-list
(print/play as notes).
Because some of the combinatorial operations were slow
to produce results, I changed the program to be of the form:
A combinatorial generator producing one small list for each call, then:
Filter/modify list through A,B, etc
Output the modified list
Call the generator for next list.
To my great surprise, this approach was often considerably _slower_ to
complete than the original program (up to ~40% depending on which modifiers
were used), despite producing initial results more quickly.
Some possibly relevant details: each sub-list consists of a short sequence of
small numbers representing musical notes, but there may be millions of them;
the modifiers range from simple tests (e.g., " if len(sub-list) is not n:
continue" ) to relatively elaborate rearrangements and transformations.
My question is, how can it be quicker to unpack and rebuild a list for each
modifier, than than to run all modifications on each element in turn?
Any explanations, comments or advice?
Thanks,
John O'Hagan
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