scipy、fftpack 和 float64 [英] scipy, fftpack and float64

问题描述

我想将 scipy.fftpack 中的 dct 功能与 numpy float64 数组一起使用.但是，它似乎仅适用于 np.float32.是否有任何快速解决方法可以完成此操作?我很快地查看了它，但我不确定所有的依赖项.所以，在搞砸一切之前，我想我会在这里寻求提示！

I would like to use the dct functionality from the scipy.fftpack with an array of numpy float64. However, it seems it is only implemented for np.float32. Is there any quick workaround I could do to get this done? I looked into it quickly but I am not sure of all the dependencies. So, before messing everything up, I thought I'd ask for tips here!

到目前为止我唯一发现的就是这个链接:http://mail.scipy.org/pipermail/scipy-svn/2010-September/004197.html

The only thing I have found so far about this is this link : http://mail.scipy.org/pipermail/scipy-svn/2010-September/004197.html

提前致谢.

这是它引发的 ValueError:

Here is the ValueError it raises:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-12-f09567c28e37> in <module>()
----> 1 scipy.fftpack.dct(c[100])

/usr/local/Cellar/python/2.7.3/lib/python2.7/site-packages/scipy/fftpack/realtransforms.pyc in dct(x, type, n, axis, norm, overwrite_x)
    118         raise NotImplementedError(
    119               "Orthonormalization not yet supported for DCT-I")
--> 120     return _dct(x, type, n, axis, normalize=norm, overwrite_x=overwrite_x)
    121 
    122 def idct(x, type=2, n=None, axis=-1, norm=None, overwrite_x=0):

/usr/local/Cellar/python/2.7.3/lib/python2.7/site-packages/scipy/fftpack/realtransforms.pyc in _dct(x, type, n, axis, overwrite_x, normalize)
    215             raise ValueError("Type %d not understood" % type)
    216     else:
--> 217         raise ValueError("dtype %s not supported" % tmp.dtype)
    218 
    219     if normalize:

ValueError: dtype >f8 not supported

推荐答案

问题不在于双精度.当然支持双精度.问题是您有一台小端计算机并且(也许从文件中加载文件?)有大端数据，请注意 dtype >f8 not supported 中的 >>.看来您只需要自己将其转换为原生 double 即可.如果您知道它的双精度，您可能只想将所有内容转换为您的本机顺序一次:

The problem is not the double precision. Double precision is of course supported. The problem is that you have a little endian computer and (maybe loading a file from a file?) have big endian data, note the > in dtype >f8 not supported. It seems you will simply have to cast it to native double yourself. If you know its double precision, you probably just want to convert everytiong to your native order once:

c = c.astype(float)

虽然我猜你也可以检查 c.dtype.byteorder ，我认为应该是 '='，如果，切换......一些东西:

Though I guess you could also check c.dtype.byteorder which I think should be '=', and if, switch... something along:

if c.dtype.byteorder != '=':
    c = c.astype(c.dtype.newbyteorder('=')) 

如果您碰巧有单精度或整数，这也应该有效...

Which should work also if you happen to have single precision or integers...

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