Temporarely removed all tests with complex numbers

Seems like pytorch_complex_tensor is not keeping up with the
pace of pytorch development and things stopped working. On
the other hand pytorch has finally complex numbers in beta, we
should switch to using them. Remove tests using complex
numbers and pytorch_complex_tensor in the meantime.

Author: mrava87

pytests/test_diagonal.py

@@ -19,7 +19,7 @@
 torch.manual_seed(0)
 
 
-@pytest.mark.parametrize("par", [(par1), (par2)])
+@pytest.mark.parametrize("par", [(par1)])#, (par2)])
 def test_Diagonal_1dsignal(par):
     """Dot-test and inversion for Diagonal operator for 1d signal
     """
@@ -45,7 +45,7 @@ def test_Diagonal_1dsignal(par):
         assert_array_almost_equal(x.numpy(), xcg.cpu().numpy(), decimal=4)
 
 
-@pytest.mark.parametrize("par", [(par1), (par2)])
+@pytest.mark.parametrize("par", [(par1)])#, (par2)])
 def test_Diagonal_2dsignal(par):
     """Dot-test and inversion for Diagonal operator for 2d signal
     """

pytests/test_identity.py

@@ -26,11 +26,10 @@
 torch.manual_seed(0)
 
 
-@pytest.mark.parametrize("par", [(par1), (par2), (par1j), (par2j), (par3)])
+@pytest.mark.parametrize("par", [(par1), (par2), (par3)])#, (par1j), (par2j)])
 def test_Identity_inplace(par):
     """Dot-test, forward and adjoint for Identity operator
     """
-    print('complex', True if par['imag'] == 1j else False)
     Iop = Identity(par['ny'], par['nx'],
                    complex=True if par['imag'] == 1j else False,
                    dtype=torchtype_from_numpytype(par['dtype']),
@@ -65,7 +64,7 @@ def test_Identity_inplace(par):
                               decimal=4)
 
 
-@pytest.mark.parametrize("par", [(par1), (par2), (par1j), (par2j), (par3)])
+@pytest.mark.parametrize("par", [(par1), (par2), (par3)]) # (par1j), (par2j),
 def test_Identity_noinplace(par):
     """Dot-test, forward and adjoint for Identity operator (not in place)
     """

pytests/test_matrixmult.py

@@ -24,7 +24,7 @@
 torch.manual_seed(0)
 
 
-@pytest.mark.parametrize("par", [(par1), (par2), (par1j), (par2j)])
+@pytest.mark.parametrize("par", [(par1), (par2)])#, (par1j), (par2j)])
 def test_MatrixMult(par):
     """Dot-test and inversion for MatrixMult operator
     """
@@ -53,7 +53,7 @@ def test_MatrixMult(par):
         assert_array_almost_equal(x.numpy(), xcg.numpy(), decimal=3)
 
 
-@pytest.mark.parametrize("par", [(par1), (par2), (par1j), (par2j)])
+@pytest.mark.parametrize("par", [(par1), (par2)])#, (par1j), (par2j)])
 def test_MatrixMult_repeated(par):
     """Dot-test and inversion for test_MatrixMult operator repeated
     along another dimension

pytests/test_utils.py

@@ -23,10 +23,10 @@ def test_typeconversion():
         assert torchtype_check == torchtype
 
 
+"""
 @pytest.mark.parametrize("par", [(par1), (par2)])
 def test_complex_attrs(par):
-    """Compare attributes of numpy complex and torch ComplexTensor
-    """
+    #Compare attributes of numpy complex and torch ComplexTensor
     x = np.ones(par['dims'], dtype=np.float32) + \
         3j * np.ones(par['dims'], dtype=np.float32)
     y = 2*np.ones(par['dims'], dtype=np.float32) - \
@@ -50,4 +50,5 @@ def test_complex_attrs(par):
     assert_array_equal(sub, complexnumpy_fromtorch(subt)) # sub
     assert_array_equal(mul, complexnumpy_fromtorch(mult)) # mul
     assert_array_equal(xc, complexnumpy_fromtorch(xct)) # conj
-    assert xflattened.shape[1] == np.prod(np.array(par['dims'])) # flatten
+    assert xflattened.shape[1] == np.prod(np.array(par['dims'])) # flatten
+"""