Do you see the O(n^2) behavior in this loop?
j = 0
while j < len(data):
word, d = fromleb(data[j:])
j += d
The slice, data[j:], creates a O(n) copy every iteration of the loop.
A bit tricky. Or at least I found it tricky to notice. Maybe because
array indexing being cheap is baked into my brain...
Long story short, this repeated slicing resulted in O(n^2) behavior in
Rbyd.fetch and probably some other functions. Even though we don't care
_too_ much about performance in these scripts, having Rbyd.fetch run in
O(n^2) isn't great.
Tweaking all from* functions to take an optional index solves this, at
least on paper.
---
In practice I didn't actually find any measurable performance gain. I
guess array slicing in Python is optimized enough that the constant
factor takes over?
(Maybe it's being helped by us limiting Rbyd.fetch to block_size in most
scripts? I haven't tested NAND block sizes yet...)
Still, it's good to at least know this isn't a bottleneck.
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