What is the difference between numpy.fft and scipy.fftpack?
Solution 1
SciPy does more:
- http://docs.scipy.org/doc/numpy/reference/routines.fft.html
- http://docs.scipy.org/doc/scipy/reference/fftpack.html#
In addition, SciPy exports some of the NumPy features through its own interface, for example if you execute scipy.fftpack.helper.fftfreq and numpy.fft.helper.fftfreq you're actually running the same code.
However, SciPy has its own implementations of much functionality. The source has performance benchmarks that compare the original NumPy and new SciPy versions. My archaic laptop shows something like this:
Fast Fourier Transform
=================================================
| real input | complex input
-------------------------------------------------
size | scipy | numpy | scipy | numpy
-------------------------------------------------
100 | 0.07 | 0.06 | 0.06 | 0.07 (secs for 7000 calls)
1000 | 0.06 | 0.09 | 0.09 | 0.09 (secs for 2000 calls)
256 | 0.11 | 0.11 | 0.12 | 0.11 (secs for 10000 calls)
512 | 0.16 | 0.21 | 0.20 | 0.21 (secs for 10000 calls)
1024 | 0.03 | 0.04 | 0.04 | 0.04 (secs for 1000 calls)
2048 | 0.05 | 0.09 | 0.08 | 0.08 (secs for 1000 calls)
4096 | 0.05 | 0.08 | 0.07 | 0.09 (secs for 500 calls)
8192 | 0.10 | 0.20 | 0.19 | 0.21 (secs for 500 calls)
It does seem that SciPy runs significantly faster as the array increases in size, though these are just contrived examples and it would be worth experimenting with both for your particular project.
It's worth checking out the source code http://www.scipy.org/Download#head-312ad78cdf85a9ca6fa17a266752069d23f785d1 . Yes those .f files really are Fortran! :-D
Solution 2
I found that numpy's 2D fft was significantly faster than scipy's, but FFTW was faster than both (using the PyFFTW bindings). Performance tests are here: code.google.com/p/agpy/source/browse/trunk/tests/test_ffts.py
And the results (for n
x n
arrays):
n sp np fftw
8: 0.010189 0.005077 0.028378
16: 0.010795 0.008069 0.028716
32: 0.014351 0.008566 0.031076
64: 0.028796 0.019308 0.036931
128: 0.093085 0.074986 0.088365
256: 0.459137 0.317680 0.170934
512: 2.652487 1.811646 0.571402
1024: 10.722885 7.796856 3.509452
Solution 3
Looking at the github respositories for each, scipy is not just importing numpy's version and renaming it (although it does borrow some functionality). You'll have to dig into the code if you want to discern the difference in implementations since the documentation doesn't make a direct comparison.
https://github.com/numpy/numpy/tree/master/numpy/fft
https://github.com/scipy/scipy/tree/master/scipy/fftpack
Charles Brunet
Master student in Electrical Engineering. My subject is about simulation of optical communication systems using auto-adaptive importance sampling methods.
Updated on December 13, 2020Comments
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Charles Brunet over 3 years
Is the later just a synonym of the former, or are they two different implementations of FFT? Which one is better?
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dtlussier over 12 yearsAny idea why they chose to maintain two different implementations?
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keflavich over 11 yearsAs a sidenote, I think the speed will depend strongly on what supporting packages you've compiled for numpy/scipy, e.g. if you've compiled BLAS/LAPACK and with which compiler and compiler flags. But I don't know which compilers/flags are faster =(
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endolith over 10 yearsscipy's fft checks if your data type is real, and uses the twice-efficient rfft if so. numpy's fft does not.
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Corvus over 10 yearsscipy returns the data in a really unhelpful format - alternating real and imaginary parts after the first element. Once you've split this apart, cast to complex, done your calculation, and then cast it all back, you lose a lot (but not all) of that speed up. Basically it is not a fair comparison - numpy's time include making the output usable, not just doing the fft.
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nick maxwell over 10 yearsAlso, check out hgomersall.github.io/pyFFTW/pyfftw/interfaces/interfaces.html It's a nice wrapper for fftw that works as a drop-in replacement for numpy.fft or scipy.fftpack.
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divenex about 7 yearsNumpy uses a C porting of the FFTPACK Fortran programs, while Scipy uses the original Fortran version.
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ZisIsNotZis over 5 yearsYou can use dtype tricks to
view
the "alternating" array as array of complex numbers (except for the first element I believe) @Corone -
fqq over 4 years@ZisIsNotZis can you expand on that?
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EntangledLoops over 3 years"It does seem that SciPy runs significantly faster..." - based on what? What are the units in your benchmark? Where is the test code? Did you use the same compilation options for the backends? If SciPy is just calling Numpy for some of these routines, the difference is just random variation.