Hi Andre,
Thanks for your message. I like it. :-)
I do have a .edu email. I didn't try to install Chaco with EPD because I
tend to be skeptical when it comes to a bundled package with a lot of
stuff. I like it to be as simple as possible. But it seems that I am
probably better off to install EPD as a whole.
Cheers,
Jianbao
On Mon, Oct 8, 2012 at 11:17 AM, Andre' Walker-Loud <walksl...@gmail.com>wrote:
> Hi Jianbao,
>
> One option for getting Chaco is to install the Enthought python
> disctribution
>
> http://www.enthought.com/
>
> you can see from their package index, they install Chaco (and all needed
> libraries to make it work)
>
> http://www.enthought.com/products/epdlibraries.php
>
> If you have an email ending in ".edu" you can automatically get their
> academic version (fully functioning version - you just have to verify you
> are doing academic research). Since you mentioned you were at UC Berkeley,
> I assume you have .edu.
> Their python installation works nicely, and installs itself in
> /Library/Frameworks/Python.framework/ so it plays nicely with the Mac GUI
> environment. Also, it will not overwrite any other installation you have -
> it makes its own install dir.
>
> UNFORTUNATELY - at the moment, it appears they are writing their new
> academic software licenses, so you can not download it right now. But
> there message promises it will soon be available again.
>
> I have found the Enthought installation to be MUCH more reliable than FINK
> or MacPorts (Enthought is also a private company - hence the quality
> installers etc, and they like to support academic work).
>
>
> Cheers,
>
> Andre
>
>
>
>
>
>
> On Oct 8, 2012, at 10:55 AM, Jianbao Tao wrote:
>
> > Hi all,
> >
> > A little background: I am from the space physics field where a lot of
> people watch/analyze satellite data for a living. This is a field currently
> dominated by IDL in terms of visualization/analysis software. I was a happy
> IDL user until I saw those very, very, I mean, seriously, very, very pretty
> matplotlib plots a couple of weeks ago. Although I was happy with IDL most
> of the time, I always hated the feel of IDL plots on screen.
> >
> > So, I decided to make my move from IDL to python + numpy + scipy +
> matplotlib. However, this is not a trivial move. One major thing that makes
> me stick to IDL in the first place is the Tplot package (bundled into
> THEMIS Data Analysis Software, a.k.a., TDAS) developed at my own lab, the
> Space Sciences Lab at UC Berkeley. I must have something equivalent to
> Tplot to work efficiently on the python platform. In order to do that,
> there are two problems to solve. First, a utility module is required to
> load data that are in NASA CDF format. Second, a 2D plotting application is
> required with the following features: 1) Able to handle large amount vector
> data, 2) able to display spectrogram with log scale axis quickly, and 3)
> convenient toolbar to navigate the data.
> >
> > I have written a module that can quickly load data in CDF files in
> cython, with help from the cython and the numpy communities. I have also
> gotten the third plotting feature working with a customized navigation
> toolbar, thanks to the help I received in this mailing list. However, I
> haven't figured out how to get the first two plotting features. Matplotlib
> is known for its slow speed when it comes to large data sets. However, it
> seems some other packages can plot large data sets very fast, although not
> as pretty as matplotlib. So, I am wondering what makes matplotlib so slow.
> Is it because the anti-aliasing engine? If so, is it possible to turn it on
> or off flexibly to compromise between performance and quality? Also, is it
> possible to convert the bottle-neck bit of the code into cython to speed up
> matplotlib? As for spectrograms with log scale axis, I found a working
> solution from Stack Overflow, but it is simply too slow. So, again, why is
> it so slow?
> >
> > So, for my purposes, my real problem now is the slow speed of
> matplotlib. I tried other packages, such as pyqtgraph, pyqwt, and
> Chaco/Traits. They seem to be faster, but they have serious problems too.
> Pyqtgraph seems very promising, but it seems to be in an infant stage for
> now with serious bugs. For example, I can't get it working together with
> matplotlib. PyQwt/guiqwt is reasonably robust, but it has too many
> dependencies in my opinion, and doesn't seem to have a wide user base.
> Chaco/Traits seems another viable possibility, especially considering the
> fact that it is actually supported by a company, but I didn't get a chance
> to see their performance and quality because I can't install Enable, a
> necessary bit for Chaco, on my mac. (But the fact that Chaco/Traits is
> supported by a real company is a real plus to me. If I can't eventually
> speed up matplotlib, I will probably give it another shot.)
> >
> > I have one idea to speed up line plots in matplotlib on screen, which is
> basically down-sampling the data before plotting. Basically, my idea is to
> down-sample the data into a level that one pixel only corresponds to one
> data point. Apparently, one must have enough information to determine the
> mapping between the data and the pixels on screen. However, such an
> overhead is just to maintain some house-keeping information, which I
> suppose is minimal.
> >
> > I have no idea how to speed up the log-scale spectrogram plot at the
> moment. :-(
> >
> > So, the bottom line: What are the options to speed up matplotlib? Your
> comments and insights are very much appreciated. :-)
> >
> > Thank you for reading.
> >
> > Cheers,
> > Jianbao
> >
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