Python numpy.fft方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def _freq(N, delta_x, real, shift):
    # calculate frequencies from coordinates
    # coordinates are always loaded eagerly, so we use numpy
    if real is None:
        fftfreq = [np.fft.fftfreq]*len(N)
    else:
        # Discard negative frequencies from transform along last axis to be
        # consistent with np.fft.rfftn
        fftfreq = [np.fft.fftfreq]*(len(N)-1)
        fftfreq.append(np.fft.rfftfreq)

    k = [fftfreq(Nx, dx) for (fftfreq, Nx, dx) in zip(fftfreq, N, delta_x)]

    if shift:
        k = [np.fft.fftshift(l) for l in k]

    return k 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def make_node(self, frames, n, axis):
        """
        Compute an n-point fft of frames along given axis.

        """
        _frames = tensor.as_tensor(frames, ndim=2)
        _n = tensor.as_tensor(n, ndim=0)
        _axis = tensor.as_tensor(axis, ndim=0)
        if self.half and _frames.type.dtype.startswith('complex'):
            raise TypeError('Argument to HalfFFT must not be complex', frames)
        spectrogram = tensor.zmatrix()
        buf = generic()
        # The `buf` output is present for future work
        # when we call FFTW directly and re-use the 'plan' that FFTW creates.
        # In that case, buf would store a CObject encapsulating the plan.
        rval = Apply(self, [_frames, _n, _axis], [spectrogram, buf])
        return rval 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def perform(self, node, inp, out):
        frames, n, axis = inp
        spectrogram, buf = out
        if self.inverse:
            fft_fn = numpy.fft.ifft
        else:
            fft_fn = numpy.fft.fft

        fft = fft_fn(frames, int(n), int(axis))
        if self.half:
            M, N = fft.shape
            if axis == 0:
                if (M % 2):
                    raise ValueError(
                        'halfFFT on odd-length vectors is undefined')
                spectrogram[0] = fft[0:M / 2, :]
            elif axis == 1:
                if (N % 2):
                    raise ValueError(
                        'halfFFT on odd-length vectors is undefined')
                spectrogram[0] = fft[:, 0:N / 2]
            else:
                raise NotImplementedError()
        else:
            spectrogram[0] = fft 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def test_size_accuracy(self):
        # Sanity check for the accuracy for prime and non-prime sized inputs
        if self.rdt == np.float32:
            rtol = 1e-5
        elif self.rdt == np.float64:
            rtol = 1e-10

        for size in LARGE_COMPOSITE_SIZES + LARGE_PRIME_SIZES:
            np.random.seed(1234)
            x = np.random.rand(size).astype(self.rdt)
            y = ifft(fft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))
            y = fft(ifft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))

            x = (x + 1j*np.random.rand(size)).astype(self.cdt)
            y = ifft(fft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))
            y = fft(ifft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def test_fft():    
    b = numpy.random.random((3,3))
    a = afnumpy.array(b)
    fassert(afnumpy.fft.fft(a), numpy.fft.fft(b))

    b = numpy.random.random((3,2))
    a = afnumpy.array(b)
    fassert(afnumpy.fft.fft(a), numpy.fft.fft(b))

    b = numpy.random.random((5,3,2))
    a = afnumpy.array(b)
    fassert(afnumpy.fft.fft(a), numpy.fft.fft(b))

    b = numpy.random.random((5,3,2))+numpy.random.random((5,3,2))*1.0j
    a = afnumpy.array(b)
    fassert(afnumpy.fft.fft(a), numpy.fft.fft(b)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def test_ifft():    
    # Real to complex inverse fft not implemented in arrayfire
    # b = numpy.random.random((3,3))
    # a = afnumpy.array(b)
    # fassert(afnumpy.fft.ifft(a), numpy.fft.ifft(b))

    # b = numpy.random.random((3,2))
    # a = afnumpy.array(b)
    # fassert(afnumpy.fft.ifft(a), numpy.fft.ifft(b))

    # b = numpy.random.random((5,3,2))
    # a = afnumpy.array(b)
    # fassert(afnumpy.fft.ifft(a), numpy.fft.ifft(b))

    b = numpy.random.random((5,3,2))+numpy.random.random((5,3,2))*1.0j
#    b = numpy.ones((3,3))+numpy.zeros((3,3))*1.0j
    a = afnumpy.array(b)
    fassert(afnumpy.fft.ifft(a), numpy.fft.ifft(b)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def test_fft2():    
    b = numpy.random.random((3,3))
    a = afnumpy.array(b)
    fassert(afnumpy.fft.fft2(a), numpy.fft.fft2(b))

    b = numpy.random.random((3,2))
    a = afnumpy.array(b)
    fassert(afnumpy.fft.fft2(a), numpy.fft.fft2(b))

    b = numpy.random.random((5,3,2))
    a = afnumpy.array(b)
    fassert(afnumpy.fft.fft2(a), numpy.fft.fft2(b))

    b = numpy.random.random((5,3,2))+numpy.random.random((5,3,2))*1.0j
    a = afnumpy.array(b)
    fassert(afnumpy.fft.fft2(a), numpy.fft.fft2(b)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def test_ifft2():    
    # Real to complex inverse fft not implemented in arrayfire
    # b = numpy.random.random((3,3))
    # a = afnumpy.array(b)
    # fassert(afnumpy.fft.ifft2(a), numpy.fft.ifft2(b))

    # b = numpy.random.random((3,2))
    # a = afnumpy.array(b)
    # fassert(afnumpy.fft.ifft2(a), numpy.fft.ifft2(b))

    # b = numpy.random.random((5,3,2))
    # a = afnumpy.array(b)
    # fassert(afnumpy.fft.ifft2(a), numpy.fft.ifft2(b))

    b = numpy.random.random((5,3,2))+numpy.random.random((5,3,2))*1.0j
#    b = numpy.ones((3,3))+numpy.zeros((3,3))*1.0j
    a = afnumpy.array(b)
    fassert(afnumpy.fft.ifft2(a), numpy.fft.ifft2(b)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def test_shape_axes_argument(self):
        small_x = [[1,2,3],[4,5,6],[7,8,9]]
        large_x1 = array([[1,2,3,0],
                                  [4,5,6,0],
                                  [7,8,9,0],
                                  [0,0,0,0]])
        # Disable tests with shape and axes of different lengths
        # y = fftn(small_x,shape=(4,4),axes=(-1,))
        # for i in range(4):
        #    assert_array_almost_equal (y[i],fft(large_x1[i]))
        # y = fftn(small_x,shape=(4,4),axes=(-2,))
        # for i in range(4):
        #    assert_array_almost_equal (y[:,i],fft(large_x1[:,i]))
        y = fftn(small_x,shape=(4,4),axes=(-2,-1))
        assert_array_almost_equal(y,fftn(large_x1))
        y = fftn(small_x,shape=(4,4),axes=(-1,-2))
        assert_array_almost_equal(y,swapaxes(
            fftn(swapaxes(large_x1,-1,-2)),-1,-2)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def _fft_module(da):
    if da.chunks:
        return dsar.fft
    else:
        return np.fft 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def isotropize(ps, fftdim, nfactor=4):
    """
    Isotropize a 2D power spectrum or cross spectrum
    by taking an azimuthal average.

    .. math::
        \text{iso}_{ps} = k_r N^{-1} \sum_{N} |\mathbb{F}(da')|^2

    where :math:`N` is the number of azimuthal bins.

    Parameters
    ----------
    ps : `xarray.DataArray`
        The power spectrum or cross spectrum to be isotropized.
    fftdim : list
        The fft dimensions overwhich the isotropization must be performed.
    nfactor : int, optional
        Ratio of number of bins to take the azimuthal averaging with the
        data size. Default is 4.
    """

    # compute radial wavenumber bins
    k = ps[fftdim[1]]
    l = ps[fftdim[0]]
    N = [k.size, l.size]
    ki, kr = _radial_wvnum(k, l, min(N), nfactor)

    # average azimuthally
    ps = ps.assign_coords(freq_r=np.sqrt(k**2+l**2))
    iso_ps = (ps.groupby_bins('freq_r', bins=ki, labels=kr).mean()
              .rename({'freq_r_bins': 'freq_r'})
             )
    return iso_ps * iso_ps.freq_r 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def _fftn_blas(f, mesh):
    Gx = np.fft.fftfreq(mesh[0])
    Gy = np.fft.fftfreq(mesh[1])
    Gz = np.fft.fftfreq(mesh[2])
    expRGx = np.exp(np.einsum('x,k->xk', -2j*np.pi*np.arange(mesh[0]), Gx))
    expRGy = np.exp(np.einsum('x,k->xk', -2j*np.pi*np.arange(mesh[1]), Gy))
    expRGz = np.exp(np.einsum('x,k->xk', -2j*np.pi*np.arange(mesh[2]), Gz))
    out = np.empty(f.shape, dtype=np.complex128)
    buf = np.empty(mesh, dtype=np.complex128)
    for i, fi in enumerate(f):
        buf[:] = fi.reshape(mesh)
        g = lib.dot(buf.reshape(mesh[0],-1).T, expRGx, c=out[i].reshape(-1,mesh[0]))
        g = lib.dot(g.reshape(mesh[1],-1).T, expRGy, c=buf.reshape(-1,mesh[1]))
        g = lib.dot(g.reshape(mesh[2],-1).T, expRGz, c=out[i].reshape(-1,mesh[2]))
    return out.reshape(-1, *mesh) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def _ifftn_blas(g, mesh):
    Gx = np.fft.fftfreq(mesh[0])
    Gy = np.fft.fftfreq(mesh[1])
    Gz = np.fft.fftfreq(mesh[2])
    expRGx = np.exp(np.einsum('x,k->xk', 2j*np.pi*np.arange(mesh[0]), Gx))
    expRGy = np.exp(np.einsum('x,k->xk', 2j*np.pi*np.arange(mesh[1]), Gy))
    expRGz = np.exp(np.einsum('x,k->xk', 2j*np.pi*np.arange(mesh[2]), Gz))
    out = np.empty(g.shape, dtype=np.complex128)
    buf = np.empty(mesh, dtype=np.complex128)
    for i, gi in enumerate(g):
        buf[:] = gi.reshape(mesh)
        f = lib.dot(buf.reshape(mesh[0],-1).T, expRGx, 1./mesh[0], c=out[i].reshape(-1,mesh[0]))
        f = lib.dot(f.reshape(mesh[1],-1).T, expRGy, 1./mesh[1], c=buf.reshape(-1,mesh[1]))
        f = lib.dot(f.reshape(mesh[2],-1).T, expRGz, 1./mesh[2], c=out[i].reshape(-1,mesh[2]))
    return out.reshape(-1, *mesh) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def _fftn_wrapper(a):
            return np.fft.fftn(a, axes=(1,2,3)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fft [as 别名]
def _ifftn_wrapper(a):
            return np.fft.ifftn(a, axes=(1,2,3))