Added tests that needed mocks

        "point_1" : (3, 6)

    }

    assert bundle.compute_sigma_sparse(V, dX, W_sensors, W_points, W_obs, column_dict) == np.sqrt(7809 / 10)

def test_compute_image_weight():

    def mock_covar(index_1, index_2):

        if index_1 != index_2:

            return 0

        return index_1 + 1

    sensor = mock.MagicMock(spec=csmapi.RasterGM)

    sensor.getParameterCovariance = mock_covar

    params = [mock.MagicMock(), mock.MagicMock(), mock.MagicMock()]

    params[0].index = 0

    params[1].index = 1

    params[2].index = 3

    np.testing.assert_allclose(

        bundle.compute_image_weight(sensor, params),

        [[1, 0,   0],

         [0, 1/2, 0],

         [0, 0,   1/4]])

def test_compute_point_weight(control_network):

    control_network.at[(control_network['id'] == 'bob').idxmax(), 'aprioriCovar'] = np.array([1, 0, 0, 2, 0, 3])

    np.testing.assert_allclose(

        bundle.compute_point_weight(control_network, 'bob'),

        [[1, 0,   0],

         [0, 1/2, 0],

         [0, 0,   1/3]])

def test_update_parameters(control_network, sensors):

    def mock_getParameterValue(index):

        return index + 1

    params = {}

    coefficient_columns = {}

    current_column = 0

    for sn, sensor in sensors.items():

        sensor.getParameterValue = mock_getParameterValue

        sensor_params = []

        for param_idx in range(6):

            param_mock = mock.MagicMock()

            param_mock.index = param_idx

            sensor_params.append(param_mock)

        params[sn] = sensor_params

        coefficient_columns[sn] = (current_column, current_column+6)

        current_column += 6

    for point_id in control_network['id'].unique():

        coefficient_columns[point_id] = (current_column, current_column+3)

        current_column += 3

    updates = np.arange(0, current_column)

    bundle.update_parameters(sensors, params, control_network, updates, coefficient_columns)

    for sn, sensor in sensors.items():

        coeff_start = coefficient_columns[sn][0]

        for param_idx in range(6):

            sensor.setParameterValue.assert_any_call(

                param_idx,

                param_idx + 1 + coeff_start + param_idx

            )

    for point_id, group in control_network.groupby('id'):

        coeff_start = coefficient_columns[point_id][0]

        coeff_end = coefficient_columns[point_id][1]

        num_measures = len(group)

        np.testing.assert_allclose(

            group[['adjustedX', 'adjustedY', 'adjustedZ']].values,

            np.tile(updates[coeff_start:coeff_end], num_measures).reshape((num_measures,3))

        )