Python fftpack.irfft方法代码示例

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [as 别名]
def test_definition(self):
        x1 = [1,2,3,4,1,2,3,4]
        x1_1 = [1,2+3j,4+1j,2+3j,4,2-3j,4-1j,2-3j]
        x2 = [1,2,3,4,1,2,3,4,5]
        x2_1 = [1,2+3j,4+1j,2+3j,4+5j,4-5j,2-3j,4-1j,2-3j]

        def _test(x, xr):
            y = irfft(np.array(x, dtype=self.rdt))
            y1 = direct_irdft(x)
            self.assertTrue(y.dtype == self.rdt,
                    "Output dtype is %s, expected %s" % (y.dtype, self.rdt))
            assert_array_almost_equal(y,y1, decimal=self.ndec)
            assert_array_almost_equal(y,ifft(xr), decimal=self.ndec)

        _test(x1, x1_1)
        _test(x2, x2_1) 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [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 = irfft(rfft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))
            y = rfft(irfft(x))
            self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
                            (size, self.rdt))

# self.ndec is bogus; we should have a assert_array_approx_equal for number of
# significant digits 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [as 别名]
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
          real=False, compute_onesided=True):
    """
    Compute ISTFT for STFT transformed X
    """
    if real:
        local_ifft = fftpack.irfft
        X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
        X_pad[:, :-1] = X
        X = X_pad
    else:
        local_ifft = fftpack.ifft
    if compute_onesided:
        X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
        X_pad[:, :fftsize // 2 + 1] = X
        X_pad[:, fftsize // 2 + 1:] = 0
        X = X_pad
    X = local_ifft(X).astype("float64")
    if step == "half":
        X = invert_halfoverlap(X)
    else:
        X = overlap_add(X, step, wsola=wsola)
    if mean_normalize:
        X -= np.mean(X)
    return X 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [as 别名]
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
          real=False, compute_onesided=True):
    """
    Compute ISTFT for STFT transformed X
    """
    if real:
        local_ifft = fftpack.irfft
        X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
        X_pad[:, :-1] = X
        X = X_pad
    else:
        local_ifft = fftpack.ifft
    if compute_onesided:
        X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
        X_pad[:, :fftsize // 2] = X
        X_pad[:, fftsize // 2:] = 0
        X = X_pad
    X = local_ifft(X).astype("float64")
    if step == "half":
        X = invert_halfoverlap(X)
    else:
        X = overlap_add(X, step, wsola=wsola)
    if mean_normalize:
        X -= np.mean(X)
    return X 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [as 别名]
def bench_random(self):
        from numpy.fft import irfft as numpy_irfft

        print()
        print('Inverse Fast Fourier Transform (real data)')
        print('==================================')
        print(' size |  scipy  |  numpy  ')
        print('----------------------------------')
        for size,repeat in [(100,7000),(1000,2000),
                            (256,10000),
                            (512,10000),
                            (1024,1000),
                            (2048,1000),
                            (2048*2,500),
                            (2048*4,500),
                            ]:
            print('%5s' % size, end=' ')
            sys.stdout.flush()

            x = random([size]).astype(double)
            x1 = zeros(size/2+1,dtype=cdouble)
            x1[0] = x[0]
            for i in range(1,size/2):
                x1[i] = x[2*i-1] + 1j * x[2*i]
            if not size % 2:
                x1[-1] = x[-1]
            y = irfft(x)

            print('|%8.2f' % measure('irfft(x)',repeat), end=' ')
            sys.stdout.flush()

            assert_array_almost_equal(numpy_irfft(x1,size),y)
            print('|%8.2f' % measure('numpy_irfft(x1,size)',repeat), end=' ')
            sys.stdout.flush()

            print(' (secs for %s calls)' % (repeat))

        sys.stdout.flush() 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [as 别名]
def test_random_real(self):
        for size in [1,51,111,100,200,64,128,256,1024]:
            x = random([size]).astype(self.rdt)
            y1 = irfft(rfft(x))
            y2 = rfft(irfft(x))
            self.assertTrue(y1.dtype == self.rdt,
                    "Output dtype is %s, expected %s" % (y1.dtype, self.rdt))
            self.assertTrue(y2.dtype == self.rdt,
                    "Output dtype is %s, expected %s" % (y2.dtype, self.rdt))
            assert_array_almost_equal(y1, x, decimal=self.ndec,
                                       err_msg="size=%d" % size)
            assert_array_almost_equal(y2, x, decimal=self.ndec,
                                       err_msg="size=%d" % size) 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [as 别名]
def test_irfft(self):
        overwritable = self.real_dtypes
        for dtype in self.real_dtypes:
            self._check_1d(irfft, dtype, (16,), -1, overwritable)
            self._check_1d(irfft, dtype, (16, 2), 0, overwritable)
            self._check_1d(irfft, dtype, (2, 16), 1, overwritable) 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [as 别名]
def test_definition(self):
        x1 = [1,2,3,4,1,2,3,4]
        x1_1 = [1,2+3j,4+1j,2+3j,4,2-3j,4-1j,2-3j]
        x2 = [1,2,3,4,1,2,3,4,5]
        x2_1 = [1,2+3j,4+1j,2+3j,4+5j,4-5j,2-3j,4-1j,2-3j]

        def _test(x, xr):
            y = irfft(np.array(x, dtype=self.rdt))
            y1 = direct_irdft(x)
            assert_equal(y.dtype, self.rdt)
            assert_array_almost_equal(y,y1, decimal=self.ndec)
            assert_array_almost_equal(y,ifft(xr), decimal=self.ndec)

        _test(x1, x1_1)
        _test(x2, x2_1) 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [as 别名]
def test_random_real(self):
        for size in [1,51,111,100,200,64,128,256,1024]:
            x = random([size]).astype(self.rdt)
            y1 = irfft(rfft(x))
            y2 = rfft(irfft(x))
            assert_equal(y1.dtype, self.rdt)
            assert_equal(y2.dtype, self.rdt)
            assert_array_almost_equal(y1, x, decimal=self.ndec,
                                       err_msg="size=%d" % size)
            assert_array_almost_equal(y2, x, decimal=self.ndec,
                                       err_msg="size=%d" % size) 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [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 = irfft(rfft(x))
            _assert_close_in_norm(x, y, rtol, size, self.rdt)
            y = rfft(irfft(x))
            _assert_close_in_norm(x, y, rtol, size, self.rdt) 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [as 别名]
def test_invalid_sizes(self):
        assert_raises(ValueError, irfft, [])
        assert_raises(ValueError, irfft, [[1,1],[2,2]], -5)


# self.ndec is bogus; we should have a assert_array_approx_equal for number of
# significant digits 

# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import irfft [as 别名]
def istft(X, windowsize=None, fftsize=None, step="half", wsola=False, mean_normalize=True,
          real=False, compute_onesided=True):
    """
    Compute ISTFT for STFT transformed X
    """
    if real:
        local_ifft = fftpack.irfft
        X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
        X_pad[:, :-1] = X
        X = X_pad
    else:
        local_ifft = fftpack.ifft
    if fftsize == None:
        assert windowsize == None
    if compute_onesided:
        X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
        X_pad[:, :fftsize // 2 + 1] = X
        X_pad[:, fftsize // 2 + 1:] = 0
        X = X_pad
    X = local_ifft(X).astype("float64")
    if step == "half":
        X = invert_halfoverlap(X)
    else:
        X = overlap_add(X, step, wsola=wsola)
    if mean_normalize:
        X -= np.mean(X)
    return X