Python pywt.array_to_coeffs() Examples

The following are 9 code examples of pywt.array_to_coeffs(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pywt , or try the search function .
Example #1
Source File: test_multilevel.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
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)
Example #2
Source File: test_multilevel.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
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)
Example #3
Source File: wavelet.py    From vampyre with MIT License 5 votes vote down vote up 
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
Example #4
Source File: DWT2D.py    From pylops with GNU Lesser General Public License v3.0 5 votes vote down vote up 
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
Example #5
Source File: DWT.py    From pylops with GNU Lesser General Public License v3.0 5 votes vote down vote up 
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
Example #6
Source File: solver_l1.py    From OneNet with GNU General Public License v3.0 5 votes vote down vote up 
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
Example #7
Source File: test_multilevel.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
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)
Example #8
Source File: test_multilevel.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
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')
Example #9
Source File: solver_l1.py    From OneNet with GNU General Public License v3.0 5 votes vote down vote up 
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