This morning I had a healthy dose of segfaults with my coffee while I fought with the NumPy ufunc API before realizing my install of NumPy was borged (or something...). After reinstalling a stable version of NumPy (1.30), I began learning about writing ufuncs.

Python lists are not efficient to operate on, since each element in the list is a PyObject. Fortunately, NumPy lists (narrays) are very efficient, since each element in the narray is just a contiguous amount of data represented by a dtype. A ufunc is an object which operates on the data in the narray.

The NumPy C API for writing ufuncs is pretty simple and fairly straightforward. The ufunc object is created using a method called PyUFunc_FromFuncAndData() which takes an array of actual generic ufunc functions (more on that later), an array of "data" ufunc C functions (more on that later), an array of signatures to tell NumPy how many arguments go into the ufunc and how many come out.

The NumPy C API comes with generic ufunc functions for iterating over data in the narray. These are things like PyUFunc_ff_f (which I assume means float + float with a result of float... which would explain my current problem...). The array of "data" ufunc C functions aforementioned refer to actual functions written in C by myself to operate on the data passed by the ufunc. The ufunc "data" function I've written will be to convert frequencies based on the frequency input. The functions must have the same parameters, but there's plenty of room to store my frequencies to be converted.

This is looking simpler than I thought. Once I get through the learning curve, that is.

## Wednesday, August 12, 2009

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