Surface plots using a 4th parameter for colormap in Mayavi

Playing with Mayavi, I wanted to find an easy way to use the colormap in an mlab.surf() call to display of fourth dimension. It is a very nice way to explore datasets. The image here below shows an option valuation tool that overlays the Black & Scholes call delta value on top of the surface made using the strikes, time to expiry and Black & Scholes call value.

How can you do it ?

NumPy/MKL vs Matlab performance

Further to the post I wrote on the MKL performance improvement on NumPy, I have tried to get some figures comparing it to Matlab. Here are some results. Any suggestion to improve the comparison is welcome. I will update it with the different values I do collect.

Here is the Matlab script I used to compare the Python code :

disp('Eig');tic;data=rand(500,500);eig(data);toc;
disp('Svd');tic;data=rand(1000,1000);[u,s,v]=svd(data);s=svd(data);toc;
disp('Inv');tic;data=rand(1000,1000);result=inv(data);toc;
disp('Det');tic;data=rand(1000,1000);result=det(data);toc;
disp('Dot');tic;a=rand(1000,1000);b=inv(a);result=a*b-eye(1000);toc;
disp('Done');

Each line is linked to the corresponding Python function in my test script (see my MKL post).

The results are interesting. Tests have been made with R2007a and R2008a versions and compared to and EPD 6.1 with NumPy using MKL.

Here are the timings on an Intel dual core computer running Matlab R2007a :

Eig : Elapsed time is 0.718934 seconds.
Svd : Elapsed time is 17.039340 seconds.
Inv : Elapsed time is 0.525181 seconds.
Det : Elapsed time is 0.200815 seconds.
Dot : Elapsed time is 0.958015 seconds.

And those are the timings on an Intel Xeon 8 cores machine running Matlab R2008a :

Eig : Elapsed time is 1.235884 seconds.
Svd : Elapsed time is 25.971139 seconds.
Inv : Elapsed time is 0.277503 seconds.
Det : Elapsed time is 0.142898 seconds.
Dot : Elapsed time is 0.354413 seconds.

Compared to the NumPy/MKL tests, here are the results :

Function	Core2Duo-With MKL	Core2Duo-R2007a	Speedup using numpy
test_eigenvalue	752ms	718ms	0.96
test_svd	4608ms	17039ms	3.70
test_inv	418ms	525ms	1.26
test_det	186ms	200ms	1.07
test_dot	666ms	958ms	1.44

Function	Xeon8core-With MKL	Xeon8core-R2008a	Speedup using numpy
test_eigenvalue	772ms	986ms	1.28
test_svd	2119ms	26081ms	12.5
test_inv	153ms	230ms	1.52
test_det	65ms	105ms	1.61
test_dot	235ms	287ms	1.23

Cloud computing with Python and Excel : picloud integration

Further to my previous post about making Python GUI interact with Excel, we thought it would be interesting to test how cloud computing could be easily accessible from Excel using Python and the picloud library.

We have just added interfaced some Python code that do compute the put and call price of a stock based on a Black & Scholes Monte-Carlo simulation. We end up with one function call in Excel :

Implementation details

Interactive Python graphics/visualisation with Excel

Making Excel users benefit of the quick and powerful Python GUI tools was an idea that looked pretty interesting.

The two main arguments for that are :

1. Python code is easily maintainable, tested and integrated compared to what can be inside of an Excel sheet
2. Python has some powerful libraries for numerical computing, 2D/3D visualisation, etc.

For the last London Financial PUG meeting (March 11), Travis and I had the idea of making Chaco playing with Excel thanks to a cool tool named pyxll and the pywin32 extension. The example allows the user to select a range of columns in Excel, send them to a Chaco regression tool where the user can select points. A Chaco tool does lively compute a regression on those points and update the Excel sheets.

The result is pretty interesting as shown on the screenshot here below. (I will most probably post a video showing how interactive it is).

Pyxll is a very interesting library allowing you to very easily make your Python function available within Excel (either as menu or functions). Thanks a lot to Tony Roberts for his excellent pieces of advice on using pyxll for the demo.

Chaco Python plotting application toolkit that facilitates writing plotting applications at all levels of complexity, from simple scripts with hard-coded data to large plotting programs with complex data interrelationships and a multitude of interactive tools. Chaco is part of the Enthought Tool Suite and available under the BDS license

Implementation details