Matthieu Brucher's blog

I am pleased to announce the first release of PyVST.
PyVST is a ctypes-based wrapper for the (open) VST standard developed by Steinberg for audio processing.

Version 0.1 provides basic access to the VST interface, as well as a script to analyze and display the audio process of a plugin. It can be easy-installed or downloaded on its Launchpad page.

Changelog for 0.1:

• Uses the VST dispatcher for several functions:
  • processReplacing
  • processDoubleReplacing
  • open/close the plugin
  • open/close the GUI editor
  • returns the GUI rectangle
  • set the sample rate
  • set the block size
  • get name/vendor/product
  • handle programs
  • handle parameters
  • suspend/resume
• set/get a parameter
• get number of programs
• get number of inputs
• get number of outputs
• display.py script
  • can load any plugin
  • displays the editor, if it exists
  • uses a stereo sine-sweep
  • displays a spectrogram of the process of the stereo sine-sweep
  • dumps properties information

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Some weeks ago, the first release of the optimization scikit was done. I’d like to expose here the internal structure and the way the implementation was thought.
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In a previous post, I’ve tried to use Qt for the editor window of a VST plugin. The thing is, I want to do more than just play with a GUI, I also want to see what is done to an audio stream by a plugin.

To do so, I’ve decided to expose the VST interface to Python. There are some implementation I’ve heard of, but they are based on Cython or other wrapping tools. Ctypes has the advantage of not needing a compilation step. There are also every functionality needed, as callback creation (plugins use a callback to ask the host some stuffs), and Python provides the additional mathematical tools to display what the plugin does. It may not be perfect, but it will be enough for a starter.
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I’m pleased to announce the first release of one of my projects. This scikits is based on a generic framework that can support unconstrained cost function minimization. It is based on a separation principle and is also completely object oriented.

Several optimizers are available:

• Nelder-Mead or simplex minimization
• Unconstrained gradient-based minimization

The usual criterias can be used:

• Iteration limit
• Parameter change (relative and absolute)
• Cost function changer (relative and absolute)
• Composite criterion generation (AND/OR)

Different direction searches are available:

• Gradient
• Several conjugate-gradient (Fletcher-Reeves, …)
• Decorators for selecting part of the gradient
• Marquardt step

Finally several line searches (1D minimization) were coded:

• Fibonacci and gold number methods (exact line searches)
• Wolfe-Powell soft and strong rules
• Goldstein line search
• Cubic interpolation

Additional helper classes can be used:

• Finite difference differentation (central and forward)
• Quadratic cost (for least square estimation)
• Levenberg-Marquardt approximation for least square estimation

Although it is the 0.1 version, the code is quite stable and is used in the learn scikit.

The package can be easy-installed or can be found on PyPI.

Several tutorials are available or will be available on the future at the following locations:

• http://matt.eifelle.com/tag/optimization/
• http://projects.scipy.org/scikits/wiki/Optimization/tutorial

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Some months ago, I’ve finished my manifold learning posts serie. As support for the manifold learning toolkit, I’ve also developed an optimization framework, which I’ll be blogging about, starting now.
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Python can be used for many things, and is mainly known for the shell scripts people wrote. Shai Vangast proposes using the langage for data analysis and visualization.
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To ease profiling and testing, I have wrapped the library with SWIG.
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GCCXML uses GCC as a front-end to parse C or C++ files. It then generates XML files for the interface, that is, it generates tags for the types and prototypes it parses. Then, pygccxml is a wrapper over it which parses the XML file to generate a Python object with every information one may need.

So I will quicly show here how it is possible to generate serialization/deserialization and then how to wrap functions with my custom serialization functions.
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I’ve promised to make an update whenever I would find a solution to the problem I had some months ago when I tried to use the latest MKL with numpy. Well, there was a simple hack that did the trick. It is far from being perfect, but at least, the tests pass now.
So the only thing you have to do is to export the LD_PRELOAD variable:

export LD_PRELOAD=/path/to/the/MKL/lib/libmkl_core.so

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IronPython is the first dynamic language developed for the .Net plateform. At first, .Net didn’t support this kind of language. This is something that keeps on coming back througout the book: you have to use some additional tricks to unleash the power of .Net dynamic and static languages.
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