Hi Jianbao, I used to try and install my python suite from src code on my own. Somewhere between the Mac OS 10.5, 10.6, migrating accounts, my python installation broke, and I never could get it all working again. Something related to 10.6 didn't have full backwards compatibility because of the switch to 64 bit architecture, so my binaries stopped working... many long frustrating days trying to figure it out. I eventually went to a friend of mine who does computing support for an astrophysics group, to get help solving my installation problems. He said, "Do you know about the Enthought python distribution?"
So that changed my philosophy. If my computer-wiz-friend uses Enthought, I have no excuse not to. I have been happier ever since :) Also - I have recently come to love HDF5 (Hierarchical Data Format (Version) 5), which is a smart binary database with smart metadata mapping (maybe good for your research). Eg. on the big machines at NERSC, Livermore, Argonne, etc (meaning next generation super computers) HDF5 is one of the pieces of software they use to benchmark the performance of their file systems, and make sure this code scales to work with these new architectures. HDF5 is also professionally maintained. And the Enthought distribution comes with HDF5 and two python interfaces to it. From your description, I thought maybe you guys already use this. And if not, maybe it is worth looking into. Cheers, Andre On Oct 8, 2012, at 11:32 AM, Jianbao Tao wrote: > 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 > > ------------------------------------------------------------------------------ > > Don't let slow site performance ruin your business. 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