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@@            Coverage Diff            @@
##             master     #127   +/-   ##
=========================================
  Coverage          ?   76.99%           
=========================================
  Files             ?       33           
  Lines             ?     1765           
  Branches          ?        0           
=========================================
  Hits              ?     1359           
  Misses            ?      406           
  Partials          ?        0

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chaithyagr commented 3 years ago

Opening for review now! The tests are passing.. Will remove the checks of CI from travis. @zaccharieramzi I feel we should move running of examples in another PR, so that we have a working pipeline to merge all the othyer PRs.

chaithyagr commented 3 years ago

The failing test has something to do with the queue implementation of the linear_op :

=================================== FAILURES ===================================
___________ TestAdjointOperatorWaveletTransform.test_Wavelet2D_PyWt ____________

self = <test_wavelet_adjoint.TestAdjointOperatorWaveletTransform testMethod=test_Wavelet2D_PyWt>

    def test_Wavelet2D_PyWt(self):
        """Test the adjoint operator for the 2D Wavelet transform
        """
        for ch in self.num_channels:
            print("Testing with Num Channels : " + str(ch))
            for i in range(self.max_iter):
                print("Process Wavelet2D PyWt test '{0}'...", i)
                wavelet_op_adj = WaveletN(
                    wavelet_name="sym8",
                    nb_scale=4,
                    n_coils=ch,
                    n_jobs=2
                )
                Img = np.squeeze(
                    np.random.randn(ch, self.N, self.N) +
                    1j * np.random.randn(ch, self.N, self.N)
                )
                f_p = wavelet_op_adj.op(Img)
                f = (np.random.randn(*f_p.shape) +
                     1j * np.random.randn(*f_p.shape))
>               I_p = wavelet_op_adj.adj_op(f)

mri/tests/test_wavelet_adjoint.py:77: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <pysap.extensions.transform.sym8 object at 0x7f7d42a5d940>
analysis_data = [array([[-0.71789798+0.40494647j,  0.92025523+1.58272514j,
        -0.35646498-1.20388159j,  0.32289798-1.23285284j,
 ...     -1.55343428e+00-1.37115525e+00j,  4.49025948e-01+5.69065856e-01j,
        -9.15547881e-01-1.51666483e+00j]]), ...]

    def _set_analysis_data(self, analysis_data):
        """ Set the decomposition coefficients array.

        Parameters
        ----------
        analysis_data: lsit of nd-array
            decomposition coefficients array.
        """
        if self.verbose > 0 and self._analysis_data is not None:
            print("[info] Replacing existing decomposition coefficients "
                  "array.")
>       if len(analysis_data) != sum(self.nb_band_per_scale):
E       TypeError: 'NoneType' object is not iterable

/usr/share/miniconda/envs/test/lib/python3.6/site-packages/pysap/base/transform.py:266: TypeError

This TypeError is spawned from bad pop from the queue implementation and its poping and pushing here:

https://github.com/CEA-COSMIC/pysap-mri/blob/3e8ae773ac7988656e30b50dd8596277ca107319/mri/operators/linear/wavelet.py#L79-L86

CEA-COSMIC / pysap-mri

Change CI to github and add fix coverage through codecov #127

Codecov Report