"James Reynolds" <eire1...@gmail.com> wrote
I've made a few other optimizations today that I won't be able to test
until
I get home, but I was wondering if any of you could give some general
pointers on how to make python run a little more quickly.
Always, always, get the algorithm efficient before trying to make
the code efficient.
Then eliminate redundant variable assignments, extra loops,
hidden loops (like in, any etc)
Then use the profiler to identify the hot spots.
Then fine tune the hot spots.
This is where you can start to worry about the speedups of
using local variables etc.
There are two major pieces of code that seem slow, so I'll share the
first
with you. This section takes up about 1/3 of the time used when running
all
trials, where trials is 10K or larger.
How are you measuring? Is it via the profiler? Is it by inserying print
time statements? Is is subjectively timing it by hand?
The second section, the one that is taking up most of the time, does the
math.
Thats probably what you would expect if the math is complex.
The list nx1 is about 220 floating point numbers long.
So not very big at all...
sample = random.sample(range(int(self.nx1[b])), trials) # a list of
sample
values based on nx1
The use of self suggests there is an object, or at least a class definition
involved?
for s in self.mcrange_gen(sample):
countlist.append(s-1) # This appends the bin number (the number
def mcrange_gen(self, sample):#, lensample):
lensample = len(sample) # this section is just for speed. All of these
are renames from the globals to bring calc times down at the expense of
memory. I haven't tested these yet.
This is premature optimisation at this stage. Its cluttering up the code
for relatively little benefit.
for s in range(lensample):
q = sample[s] #takes the next randomly generated number from the
for q in sample
would be more pythonic
nx2_append(q) # and appends it to nx list.
nx2_sort() # and sorts it in place
nx2_reverse() # reverses the list, because this was the original
So you sort and reverse the entire list every time round the for loop?
Might it be more efficient to keep the list in the right order to start
with?
i = nx2_index(q) #get the index of that element
nx2_remove(q) # and remove the element.
Now you find the thing you inserted and remove it. Wow.
yield i # send the position of that element back to the main
So you really just want to find out where you would like to insert it
in an already sorted/reversed list?
Back to step one - can you improve the algorithm?
--
Alan Gauld
Author of the Learn to Program web site
http://www.alan-g.me.uk/
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