Re: [Matplotlib-users] Development advice needed

[Sterling S. <sm...@fu...>]

Jianbao,

The one thing I would add to Anthony's response, which is a good summary of what I would say, is that you should look into the animation aspects of matplotlib, and the xdata and ydata attributes of lines/axes for speed in replotting mostly similar situations.  I regret having not learned of these before I made an application, and I have yet to go back and implement these faster methods.

-Sterling

On Oct 3, 2012, at 9:49AM, Anthony Floyd wrote:

> Hi Jianbao,
> 
> First some context: at the company I work for, we've been using
> matplotlib to do much of what you want to do for the past 4 years. We
> have created our own application for plotting, interrogating, and
> manipulating time-series data coming from both simulations and
> measurements, although from a completely different domain (in our case
> it's virtual manufacturing of composite materials). In the past two
> years, we've also been using matplotlib to plot in more-or-less
> realtime data from a cloud industrial sensors (temperature, pressure,
> etc).
> 
>> After reading the matplotlib documents and trying out several little
>> examples for a few days, I now have a feeling that matplotlib at least has
>> most of the infrastructure ready for my purposes. One thing that bothers me
>> a little bit is that the plotting speed seems to be a little slow. But IDL
>> had the same problem in the first place too. As computers became faster and
>> faster, that problem just became less and less important. I expect the same
>> thing will happen to matplotlib too.
> 
> This is true, matplotlib can be slow, particularly for large data sets
> and many data sets. The trick is to downsample (and use tiling if
> you're going to be panning around a lot) what you're actually plotting
> before handing it off to the plot. I think more recent versions of
> matplotlib handle some of this for you, but we've found that it's
> faster to do the downsampling ourselves.
> 
>> Now let me turn to technical stuff. What I want is a time-series plotting
> [...]
>> sufficient. Third, the system should have minimal dependencies for the sake
>> of portability and installation easiness. As for now, I don't want any
>> dependencies beyond numpy, scipy, and matplotlib. Ipython would be a highly
>> recommended tool, but the system should be just fine without it.
> 
> You're going to need more than that. At the very least you're going to
> need a widget framework like wxPython, pyQT, pyGTK, or some such.
> These will provide you with all the window management, widget
> controls, and so on. Our preference is wxPython but YMMV.
> 
>> After weighing all the options, I sense that I will probably be better off
>> to use the matplotlib library directly, rather than the convenient utilities
>> provided by pyplot. However, I am having a hard time to find good
>> instructions for using the matplotlib infrastructure. So, I would like to
>> hear some references on that. I also would like to hear general advice about
>> how to construct such a system so that its structure is consistent with
>> matplotlib conventions. Other comments and advice are warmly welcome too.
> 
> Absolutely, you'll want to use the API rather than the utility
> functions. The best reference for that is the online documentation at
> matplotlib.org. In the past we've found the source code documentation
> (or, say, that generated by doxygen) more helpful than the Sphinx
> documentation, but frankly our matplotlib bits are pretty stable now
> and we haven't had to use the documentation for a while (perhaps it's
> better now).
> 
> Good luck! We've been very happy with our design choices, and get
> nothing but positive feedback on how our plots look and feel.
> matplotlib and the amazing active community around it have everything
> to do with that.
> 
> Anthony.
> 
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