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Posts Tagged ‘numpy’

pyVST – still tinkering . .

This post is more a note to myself than a post of general interest. It is also to let people know that I’m still fiddling with pyVST now and then. I’ve been looking at how to build C extensions to python when using numpy using the following link. I’ve got these extensions working in windows by downloading the tarball and compiling the examples using codeblocks.

The examples are C extensions for a python script but what I’m aiming to do with pyVST, is get the plugin dll to start up the python engine and then pass each frame of the data buffer to a python script for processing. The C extension code will make a useful starting point for passing the data between the plugin dll and python. The aim is to make the plugin aware of its file name and then call a python script with the same filename to do the processing. This will eventually alow a user to make plugins without having to touch any C code. I’ve got a bit of time off at the end of this month, so maybe pyVST will get moving a bit more.

There is no documentation on the numpy extensions page for building on a Windows platform, so here is what I did:

  • Download the tarball and extract
  • Include the C source and header in your poject
  • Include the numpy headers directory
  • Link to the python library
  • Add this missing line to the header file “static PyObject *rowx2_v2(PyObject *self, PyObject *args);”
  • In the main source file, ptrvector() allocates n*sizeof(double), but should really allocate pointers to double; so: n*sizeof(double *)
  • Set your compiler to build a dll
  • Name the output file “_C_arraytest.pyd
  • Put the output file in the same dir as C_arraytest.py and run

Hope this helps anyone googling C extensions for numpy under Windows 🙂

Categories: Python Tags: , , , , ,

pyvst – those pesky numpy data types

This is more of a note for myself than an actual blog post, but I’ve found a useful little resource to help get the numpy data typed to talk with the vst sdk. I plan on doing all of the numerical processing using numpy arrays and the like to make rapid development and testing easy. see the resource here. Sorry to anyone checking up on this about the delay since previous posts. I’m pretty snowed under with studies at the moment and don’t have much time to spend here as a consequence. I’m still alive though!!

Categories: Python Tags: ,