Additional coverage updates for transformations

        os.remove('test_save.cg')

    def test_save_load_features(self):

        for i in ['all_out.hdf', 'one_out.hdf']:

            try:

                os.remove(i)

            except: pass

        graph = self.graph.copy()

        graph.extract_features(extractor_parameters={'nfeatures': 10})

        graph.save_features('all_out.hdf')

        self.assertAlmostEqual(error.x_rms, 0.0, 1)

        self.assertAlmostEqual(error.y_rms, 0.0, 1)

    def test_Homography_fail(self):

        self.assertRaises(TypeError, transformations.Homography, [1, 2, 3], 'a', 'b')

        description = H.describe_error

        self.assertIsInstance(description, pd.DataFrame)

    def test_Homography_fail(self):

        with self.assertRaises(TypeError):

            h = transformations.Homography([1,2,3], np.arange(3), np.arange(3), None)

        with self.assertRaises(ValueError):

            h = transformations.Homography(np.arange(4).reshape(2,2),

                                           np.arange(3), np.arange(3), None)

class TestFundamentalMatrix(unittest.TestCase):

    @abc.abstractmethod

    def __new__(cls, inputarr, x1, x2, mask):

        obj = np.asarray(inputarr).view(cls)

        if not isinstance(inputarr, np.ndarray):

            raise TypeError('The homography must be an ndarray')

        if not inputarr.shape[0] == 3 and not inputarr.shape[1] == 3:

            raise ValueError('The homography must be a 3x3 matrix.')

        obj = np.asarray(inputarr).view(cls)

        obj.x1 = x1

        obj.x2 = x2

        obj.mask = mask

    @abc.abstractproperty

    def describe_error(self):

        if not getattr(self, '_error', None):

            self._error = self.compute_error(self.x1,

                                             self.x2,

                                             self.mask)

        return self.error.describe()

    @abc.abstractmethod