Description:

ANFFT is intended to be used in situations where large numbers
of expensive FFTs must be performed, and for which the speed
of the built-in NumPy or SciPy functions has been found insufficient.

By default, ANFFT provides immediate results by using FFTW's
"estimate" mode, which does not require tuning and is still very fast.
However, each high-level function provides a keyword named "measure"
which will invoke the full FFTW planning machinery.
FFTW will investigate which implementations are fastest on your
hardware for the given problem size, and ANFFT will transparently
cache this information to speed up later transforms on arrays of
the same size and type. Accumulated FFTW "wisdom" about which
methods work best is stored across Python sessions in a configuration
file in ~/.anfft