Eric Firing wrote:
> David,
>
> I have made some changes in svn that address all but one of the points
> you made:
>
> [....]
>> if self.clip:
>> mask = ma.getmaskorNone(val)
>> if mask == None:
>> val = ma.array(clip(val.filled(vmax), vmin, vmax))
>> else:
>> val = ma.array(clip(val.filled(vmax), vmin, vmax),
>> mask=mask)
>
> The real problem here is that I should not have been using
> getmaskorNone(). In numpy.ma, we need nomask, not None, so we want an
> ordinary getmask() call. ma.array(...., mask=ma.nomask) is very fast,
> so the problem goes away.
>
>>
>> Actually, the problem is in ma.array: with a value of mask to None,
>> it should not make a difference between mask = None or no mask arg,
>> right ?
> But it does, because for numpy it needs to be nomask; it does
> something with None, but whatever it is, it is very slow.
>
>> I didn't change ma.array to keep my change as local as possible. To
>> change only this operation as above gives a speed up from 1.8 s to ~
>> 1.0 s for to_rgba, which means calling show goes from ~ 2.2 s to ~1.4
>> s. I also changed result = (val-vmin)/float(vmax-vmin)
>>
>> to
>>
>> invcache = 1.0 / (vmax - vmin)
>> result = (val-vmin) * invcache
>
> This is the one I did not address. I don't understand how this could
> be making much difference, and some testing using ipython and %prun
> with 1-line operations showed little difference with variations on
> this theme. The fastest would appear to be (and logically should be,
> I think) result = (val-vmin)*(1.0/(vmax-vmin)), but I don't think it
> makes much difference--it looks to me like maybe 10-20 msec, not 100,
> on my Pentium M 1.6 Ghz. Maybe still worthwhile, so I may yet make
> the change after more careful testing.
>
>
>>
>> which gives a moderate speed up (around 100 ms for a 8000x256 points
>> array). Once you make both those changes, the clip call is by far the
>> most expensive operation in normalize functor, but the functor is not
>> really expensive anymore compared to the rest, so this is not where I
>> looked at.
>>
>> For the where calls in Colormap functor, I was wondering if they are
>> necessary in all cases: some of those calls seem redundant, and it
>> may be possible to detect that before calling them. This should be
>> both easier and faster, at least in this case, than having a fast
>> where ?
>>
>
> You hit the nail squarely: where() is the wrong function to use, and I
> have eliminated it from colors.py. The much faster replacement is
> putmask, which does as well as direct indexing with a Boolean but
> works with all three numerical packages. I think that using the fast
> putmask is better than trying to figure out special cases in which
> there would be nothing to put, although I could be convinced otherwise.
>
>
>> I understand that support of multiple array backend, support of mask
>> arrays have cost consequences. But it looks like it may be possible
>> to speed things up for cases where an array has only meaningful
>> values/no mask.
>
> The big gains here were essentially bug fixes--picking the appropriate
> function (getmask versus getmaskorNone and putmask versus where).
Ok, I've installed last svn, and now, there is still one function which
is much slower than a direct numpy implementation, so I would like to
know if this is inherent to the multiple backend nature of matplotlib or
not. The functor Normalize uses the clip function, and a direct numpy
would be 3 times faster (giving the show call a 20 % speed in my really
limited benchmarks):
if clip:
mask = ma.getmask(val)
#val = ma.array(nx.clip(val.filled(vmax), vmin, vmax),
# mask=mask)
def myclip(a, m, M):
a[a<m] = m
a[a>M] = M
return a
val = ma.array(myclip(val.filled(vmax), vmin, vmax), mask=mask)
I am a bit lost in the matplotlib code to see where clip is implemented
(is it in numerix and as such using the numpy function clip ?).
Still, I must confess that all this looks quite good, because it was
possible to speed things up quite considerably without too much effort,
cheers,
David
-------------------------------------------------------------------------
Take Surveys. Earn Cash. Influence the Future of IT
Join SourceForge.net's Techsay panel and you'll get the chance to share your
opinions on IT & business topics through brief surveys - and earn cash
http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users
