Python pywt.array_to_coeffs方法代码示例

# 需要导入模块: import pywt [as 别名]
# 或者: from pywt import array_to_coeffs [as 别名]
def test_wavedecn_coeff_reshape_even():
    # verify round trip is correct:
    #   wavedecn - >coeffs_to_array-> array_to_coeffs -> waverecn
    # This is done for wavedec{1, 2, n}
    rng = np.random.RandomState(1234)
    params = {'wavedec': {'d': 1, 'dec': pywt.wavedec, 'rec': pywt.waverec},
              'wavedec2': {'d': 2, 'dec': pywt.wavedec2, 'rec': pywt.waverec2},
              'wavedecn': {'d': 3, 'dec': pywt.wavedecn, 'rec': pywt.waverecn}}
    N = 28
    for f in params:
        x1 = rng.randn(*([N] * params[f]['d']))
        for mode in pywt.Modes.modes:
            for wave in wavelist:
                w = pywt.Wavelet(wave)
                maxlevel = pywt.dwt_max_level(np.min(x1.shape), w.dec_len)
                if maxlevel == 0:
                    continue

                coeffs = params[f]['dec'](x1, w, mode=mode)
                coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs)
                coeffs2 = pywt.array_to_coeffs(coeff_arr, coeff_slices,
                                               output_format=f)
                x1r = params[f]['rec'](coeffs2, w, mode=mode)

                assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4) 

# 需要导入模块: import pywt [as 别名]
# 或者: from pywt import array_to_coeffs [as 别名]
def test_waverecn_coeff_reshape_odd():
    # verify round trip is correct:
    #   wavedecn - >coeffs_to_array-> array_to_coeffs -> waverecn
    rng = np.random.RandomState(1234)
    x1 = rng.randn(35, 33)
    for mode in pywt.Modes.modes:
        for wave in ['haar', ]:
            w = pywt.Wavelet(wave)
            maxlevel = pywt.dwt_max_level(np.min(x1.shape), w.dec_len)
            if maxlevel == 0:
                continue
            coeffs = pywt.wavedecn(x1, w, mode=mode)
            coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs)
            coeffs2 = pywt.array_to_coeffs(coeff_arr, coeff_slices)
            x1r = pywt.waverecn(coeffs2, w, mode=mode)
            # truncate reconstructed values to original shape
            x1r = x1r[[slice(s) for s in x1.shape]]
            assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4) 

# 需要导入模块: import pywt [as 别名]
# 或者: from pywt import array_to_coeffs [as 别名]
def recon(self,z1):
        """
        Wavelet reconstruction:  coefficients -> image
        """
        coeffs = pywt.array_to_coeffs(z1, self.coeff_slices, \
            output_format='wavedec2')
        z0 = pywt.waverec2(coeffs, wavelet=self.wavelet, mode=self.mode)
        return z0 

# 需要导入模块: import pywt [as 别名]
# 或者: from pywt import array_to_coeffs [as 别名]
def _rmatvec(self, x):
        x = np.reshape(x, self.dimsd)
        x = pywt.array_to_coeffs(x, self.sl, output_format='wavedec2')
        y = pywt.waverec2(x, wavelet=self.waveletadj, mode='periodization',
                          axes=self.dirs)
        y = self.pad.rmatvec(y.ravel())
        return y 

# 需要导入模块: import pywt [as 别名]
# 或者: from pywt import array_to_coeffs [as 别名]
def _rmatvec(self, x):
        if self.reshape:
            x = np.reshape(x, self.dimsd)
        x = pywt.array_to_coeffs(x, self.sl, output_format='wavedecn')
        y = pywt.waverecn(x, wavelet=self.waveletadj, mode='periodization',
                          axes=(self.dir, ))
        y = self.pad.rmatvec(y.ravel())
        return y 

# 需要导入模块: import pywt [as 别名]
# 或者: from pywt import array_to_coeffs [as 别名]
def inverse_wavelet_transform(w_coeffs_rgb, coeff_slices, x_shape):
    x_hat = np.zeros(x_shape)
    for i in range(w_coeffs_rgb.shape[0]):
        w_coeffs_list = pywt.array_to_coeffs(w_coeffs_rgb[i,:,:], coeff_slices)
        x_hat[0,:,:,i] = pywt.waverecn(w_coeffs_list, wavelet='db4', mode='periodization')
    return x_hat 

# 需要导入模块: import pywt [as 别名]
# 或者: from pywt import array_to_coeffs [as 别名]
def test_wavedecn_coeff_reshape_axes_subset():
    # verify round trip is correct when only a subset of axes are transformed:
    #   wavedecn - >coeffs_to_array-> array_to_coeffs -> waverecn
    # This is done for wavedec{1, 2, n}
    rng = np.random.RandomState(1234)
    mode = 'symmetric'
    w = pywt.Wavelet('db2')
    N = 16
    ndim = 3
    for axes in [(-1, ), (0, ), (1, ), (0, 1), (1, 2), (0, 2), None]:
        x1 = rng.randn(*([N] * ndim))
        coeffs = pywt.wavedecn(x1, w, mode=mode, axes=axes)
        coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs, axes=axes)
        if axes is not None:
            # if axes is not None, it must be provided to coeffs_to_array
            assert_raises(ValueError, pywt.coeffs_to_array, coeffs)

        # mismatched axes size
        assert_raises(ValueError, pywt.coeffs_to_array, coeffs,
                      axes=(0, 1, 2, 3))
        assert_raises(ValueError, pywt.coeffs_to_array, coeffs,
                      axes=())

        coeffs2 = pywt.array_to_coeffs(coeff_arr, coeff_slices)
        x1r = pywt.waverecn(coeffs2, w, mode=mode, axes=axes)

        assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4) 

# 需要导入模块: import pywt [as 别名]
# 或者: from pywt import array_to_coeffs [as 别名]
def test_array_to_coeffs_invalid_inputs():
    coeffs = pywt.wavedecn(np.ones(2), 'haar')
    arr, arr_slices = pywt.coeffs_to_array(coeffs)

    # empty list of array slices
    assert_raises(ValueError, pywt.array_to_coeffs, arr, [])

    # invalid format name
    assert_raises(ValueError, pywt.array_to_coeffs, arr, arr_slices, 'foo')