matplotlib-devel Mailing List for matplotlib

I wonder if the tick marks could take advantage of advantages of
Line2DCollection (if it hasn't already), or maybe go so far as to have them
be PatchCollections? We could maintain full feature set and such, but take
advantage of some of the optimized rendering pathways in the backends that
were originally made for plot()?
Just thinking off the top of my head at the moment.
Cheers!
Ben Root
On Wed, Jul 9, 2014 at 12:54 PM, Joel B. Mohler <jo...@ki...>
wrote:
> On 07/08/2014 11:33 AM, Bartosz wrote:
> > Hi,
> >
> > When improving the performance of plotting high-dimensional data using
> > faceted scatter plots, I noticed that much of time was spent on the axis
> > creation (even 50%!).
> >
> > On my machine creating 20x20 array of subplots without actually plotting
> > anything takes about 11 seconds (for comparison plotting 5000 points on
> > all of them takes only 0.6s!):
> >
> > import matplotlib
> > matplotlib.interactive(True)
> > import matplotlib.pyplot as plt
> > fig, axes = plt.subplots(20,20)
> > plt.show()
> >
> > Profiling shows that 50% of computation time is spent on axis/ticks
> > creation [1], which I have to remove anyways. Is there any easy way of
> > creating thinned axes without ticks and spines?
> >
> > So far I solved the problem by subclassing Axes class (see this gist
> > [2]) and removing all spines and ticks. Running the above example gives
> > a 10x boost in performance (from 11s to 0.9s).
> >
> > import thin_axes
> > fig, axes = plt.subplots(20,20, subplot_kw=dict(projection='thin'))
> > plt.show()
>
> Hi, I also have found tick marks to be a real performance drain and am
> trying to fix this. I have yet to get my ideas all in a shape which is
> worthy of a pull request. It's a rather large change under the hood and
> so there are probably quite a few edge cases which I'm not really aware
> of since I'm sure I only care about 50% (or less) of the full range of
> flexibility. That said, simple graphs with basic tick marks are much
> slower than they need to be.
>
> My work is at https://github.com/jbmohler/mplfastaxes and I also used
> the custom projection method to replace the Axes/Axis classes. I have
> incorporated your example because I think it is interesting (even
> through 20x20 grid of axes seems crazy to me ... it may make sense
> though :) ).
>
> You have addressed a somewhat different case than myself because I've
> focused on the speed of drawing the graphics where-as your gist
> illustrates that making a new figure with many axes is very slow. I
> believe the same ideas apply and I'm going to spend some time right now
> improving my code's initialization which is basically unchanged from MPL
> at this point.
>
> Joel
>
>
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On 07/08/2014 11:33 AM, Bartosz wrote:
> Hi,
>
> When improving the performance of plotting high-dimensional data using
> faceted scatter plots, I noticed that much of time was spent on the axis
> creation (even 50%!).
>
> On my machine creating 20x20 array of subplots without actually plotting
> anything takes about 11 seconds (for comparison plotting 5000 points on
> all of them takes only 0.6s!):
>
> import matplotlib
> matplotlib.interactive(True)
> import matplotlib.pyplot as plt
> fig, axes = plt.subplots(20,20)
> plt.show()
>
> Profiling shows that 50% of computation time is spent on axis/ticks
> creation [1], which I have to remove anyways. Is there any easy way of
> creating thinned axes without ticks and spines?
>
> So far I solved the problem by subclassing Axes class (see this gist
> [2]) and removing all spines and ticks. Running the above example gives
> a 10x boost in performance (from 11s to 0.9s).
>
> import thin_axes
> fig, axes = plt.subplots(20,20, subplot_kw=dict(projection='thin'))
> plt.show()
Hi, I also have found tick marks to be a real performance drain and am 
trying to fix this. I have yet to get my ideas all in a shape which is 
worthy of a pull request. It's a rather large change under the hood and 
so there are probably quite a few edge cases which I'm not really aware 
of since I'm sure I only care about 50% (or less) of the full range of 
flexibility. That said, simple graphs with basic tick marks are much 
slower than they need to be.
My work is at https://github.com/jbmohler/mplfastaxes and I also used 
the custom projection method to replace the Axes/Axis classes. I have 
incorporated your example because I think it is interesting (even 
through 20x20 grid of axes seems crazy to me ... it may make sense 
though :) ).
You have addressed a somewhat different case than myself because I've 
focused on the speed of drawing the graphics where-as your gist 
illustrates that making a new figure with many axes is very slow. I 
believe the same ideas apply and I'm going to spend some time right now 
improving my code's initialization which is basically unchanged from MPL 
at this point.
Joel