Dear Anthony,
Thank you so much for your advice. I embedded my response below.
Jianbao
On Wed, Oct 3, 2012 at 10:49 AM, Anthony Floyd <anthonyfl...@gmail.com>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).
>
Do you have any references, such as screen shots, gallery, examples, or
whatever? I am very curious to see what people can do with matplotlib.
> > 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.
>
As a matter of fact, I considered writing intermediate routines to handle
downsampling before feeding data in matplotlib. However, you will have to
do anti-alias filtering for that. So, I wasn't sure downsampling would
boost the speed anyway. But based on your experience, this is probably a
good idea. :-)
>
> > 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.
>
One of my concerns about third-party widget framework is that sometimes it
is difficult to install them. In fact, I tried to install wxPython on my
Mac (10.8 OS X) last night, but didn't succeed. Another concern of mine is
that I don't know how efficient or how easy to interact with a thrid-party
widget framework from a python interpreter. However, again, based on your
reply, it doesn't seem to be a big issue after all.
>
> > 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.
>
I am very glad to hear that. :-)
>
> Anthony.
>
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