numpy之间有什么区别?fft和scipy.fftpack吗?-Whatisthedifferencebetweennumpy.fftandscipy.fftpack?

33  

SciPy does more:

SciPy更多:

• http://docs.scipy.org/doc/numpy/reference/routines.fft.html
• http://docs.scipy.org/doc/numpy/reference/routines.fft.html
• http://docs.scipy.org/doc/scipy/reference/fftpack.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.

此外，SciPy通过它自己的接口导出一些NumPy特性，例如，如果您执行SciPy .fftpack.helper。fftfreq numpy.fft.helper。fftfreq实际上运行的是相同的代码。

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:

然而，SciPy有自己的许多功能实现。源代码有性能基准，比较原始的NumPy和新的SciPy版本。我的老式笔记本电脑显示了如下内容:

                 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.

似乎SciPy随着数组大小的增加运行得更快，尽管这些只是人为设计的示例，对于您的特定项目来说，这两者都值得尝试。

It's worth checking out the source code http://www.scipy.org/Download#head-312ad78cdf85a9ca6fa17a266752069d23f785d1 . Yes those .f files really are Fortran! :-D

这是值得一看的源代码http://www.scipy.org/Download#头-312ad78cdf85a9ca6fa17a266752069d23f785d1。是的，那些.f文件真的是Fortran!:- d

16  

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

我发现numpy的2D fft明显比scipy快，但是FFTW比两者都快(使用PyFFTW绑定)。性能测试在这里:code.google.com/p/agpy/source/browse/trunk/tests/test_ffts.py

And the results (for n x n arrays):

结果(n x n个数组):

           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

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.

考虑到github对每个用户的贡献，scipy不仅仅是导入numpy的版本并重新命名它(尽管它确实借用了一些功能)。如果您想要了解实现的差异，那么您就必须深入研究代码，因为文档并没有直接比较。

https://github.com/numpy/numpy/tree/master/numpy/fft

https://github.com/numpy/numpy/tree/master/numpy/fft

https://github.com/scipy/scipy/tree/master/scipy/fftpack

https://github.com/scipy/scipy/tree/master/scipy/fftpack