Hi Eric,
Not sure if this is exactly what Jinbao is referring to, but:
ax=gca()
X = randn(300,300)
pcolor(X)
ax.set_yscale('log')
ylim(1,100)
Brings my computer to a standstill for over a minute. Removing the
"set_yscale" command speeds things up a lot.
Same thing takes about 2 s in Matlab.
Cheers, Jody
On Oct 8, 2012, at 11:29 AM, Eric Firing <efir...@hawaii.edu> wrote:
> On 2012/10/08 7:55 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
>> <http://themis.ssl.berkeley.edu/software.shtml>) 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 fromStack Overflow
>> <http://stackoverflow.com/questions/10812189/creating-a-log-frequency-axis-spectrogram-using-specgram-in-matplotlib>,
>> 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. :-(
>
> For each type of plot, I suggest you provide a very minimal script,
> generating its own fake data, that illustrates the problem and that can
> serve as a benchmark and test jig for speed-ups. Without these
> examples, it is somewhere between difficult and impossible for anyone to
> make useful suggestions.
>
> Eric
>
>>
>> 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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Jody Klymak
http://web.uvic.ca/~jklymak/
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