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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