John Hunter wrote:
>
>
> David> In make_image, most of the time is taken into to_rgba:
> David> almost half of it is taken in by the take call in the
> David> Colormap.__call__. Almost 200 ms to get colors from the
> David> indexes seems quite a lot (this means 280 cycles / pixel on
> "David" == David Cournapeau <[EMAIL PROTECTED]> writes:
David> In make_image, most of the time is taken into to_rgba:
David> almost half of it is taken in by the take call in the
David> Colormap.__call__. Almost 200 ms to get colors from the
David> indexes seems quite a lot
David Cournapeau wrote:
> Eric Firing wrote:
>> There is a clip function in all three numeric packages, so a native
>> clip is being used.
>>
>> If numpy.clip is actually slower than your version, that sounds like a
>> problem with the implementation in numpy. By all logic a single clip
>> func
Eric Firing wrote:
>
> There is a clip function in all three numeric packages, so a native
> clip is being used.
>
> If numpy.clip is actually slower than your version, that sounds like a
> problem with the implementation in numpy. By all logic a single clip
> function should either be the same
David Cournapeau wrote:
[...]
> 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 fu
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))
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:
>
Eric Firing wrote:
>
> Regarding the clip line, I think that your test for mask is None is
> not the right solution because it knocks out the clipping operation,
> but the clipping is intended regardless of the state of the mask. I
> had expected it to be a very fast operation, so I am surpris
Eric Firing wrote:
> Regarding the clip line, I think that your test for mask is None is not
> the right solution because it knocks out the clipping operation, but the
> clipping is intended regardless of the state of the mask. I had
> expected it to be a very fast operation,
for what it's wo
David,
>- first, we can see that in expose_event (one is expensive, the other
> negligeable, from my understanding), two calls are pretty expensive:
> the __call__ at line 735 (for normalize functor) and one for __call__
> at line 568 (for colormap functor).
>- for normalize functor, o
David Cournapeau wrote:
> But the show case is more interesting:
>
> ncalls tottime percall cumtime percall filename:lineno(function)
> 10.0020.0023.8863.886
> slowmatplotlib.py:177(bench_imshow_show)
> 10.0000.0003.8843.884
> slowmatplotlib.py:163
John Hunter wrote:
This is where you can help us. Saying specgram is slow is only
marginally more useful than saying matplotlib is slow or python is
slow. What is helpful is to post a complete, free-standing script
that we can run, with some attached performance numbers. For
starters, just run
On 12/12/06, John Hunter <[EMAIL PROTECTED]> wrote:
> --verbose-helpful will confirm the setting). A good way to start is
> to write a demonstration script that you find too slow which makes a
> call to savefig, and run it with
>
> > time myscript.py --verbose-helpful -dAgg
It may be worth men
> "David" == David Cournapeau <[EMAIL PROTECTED]> writes:
David> Hi, I am a regular user of matplotlib since I moved from
David> matlab to python/numpy/scipy. Even if I find matplotlib to
David> be a real help during the transition from matlab to python,
David> I must confess I
Hi,
I am a regular user of matplotlib since I moved from matlab to
python/numpy/scipy. Even if I find matplotlib to be a real help during
the transition from matlab to python, I must confess I found it the most
disappointing compare other packages ( essentially numpy/scipy/ipython).
This i
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