Loading knoten/bundle.py +4 −4 Original line number Diff line number Diff line Loading @@ -452,7 +452,7 @@ def compute_sigma(V, dX, W_parameters, W_observations): Parameters ---------- V : np.array V : ndarray An array of residuals of the difference between registered measure and back projected ground points in image space. dX : ndarray Loading tests/test_bundle.py +28 −0 Original line number Diff line number Diff line Loading @@ -171,3 +171,31 @@ def test_compute_residuals(control_network, sensors): V = bundle.compute_residuals(control_network, sensors) assert V.shape == (18,) np.testing.assert_allclose(V, [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1]) def test_compute_sigma0(): V = np.arange(0, 16) + 1 W_obs = np.diag(np.arange(16, 0, -1)) W_params = np.array( [[1, 2, 3, 0, 0, 0], [4, 5, 6, 0, 0, 0], [7, 8, 9, 0, 0, 0], [0, 0, 0, -1, -2, -3], [0, 0, 0, -4, -5, -6], [0, 0, 0, -7, -8, -9]] ) dX = np.arange(-6, 0) assert bundle.compute_sigma(V, dX, W_params, W_obs) == np.sqrt(7809 / 10) def test_compute_sigma0_sparse(): V = np.arange(0, 16) + 1 W_obs = np.diag(np.arange(16, 0, -1)) W_sensors = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) W_points = { "point_1" : np.array([[-1, -2, -3], [-4, -5, -6], [-7, -8, -9]]) } dX = np.arange(-6, 0) column_dict = { "image_1" : (0, 3), "point_1" : (3, 6) } assert bundle.compute_sigma_sparse(V, dX, W_sensors, W_points, W_obs, column_dict) == np.sqrt(7809 / 10) Loading
knoten/bundle.py +4 −4 Original line number Diff line number Diff line Loading @@ -452,7 +452,7 @@ def compute_sigma(V, dX, W_parameters, W_observations): Parameters ---------- V : np.array V : ndarray An array of residuals of the difference between registered measure and back projected ground points in image space. dX : ndarray Loading
tests/test_bundle.py +28 −0 Original line number Diff line number Diff line Loading @@ -171,3 +171,31 @@ def test_compute_residuals(control_network, sensors): V = bundle.compute_residuals(control_network, sensors) assert V.shape == (18,) np.testing.assert_allclose(V, [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1]) def test_compute_sigma0(): V = np.arange(0, 16) + 1 W_obs = np.diag(np.arange(16, 0, -1)) W_params = np.array( [[1, 2, 3, 0, 0, 0], [4, 5, 6, 0, 0, 0], [7, 8, 9, 0, 0, 0], [0, 0, 0, -1, -2, -3], [0, 0, 0, -4, -5, -6], [0, 0, 0, -7, -8, -9]] ) dX = np.arange(-6, 0) assert bundle.compute_sigma(V, dX, W_params, W_obs) == np.sqrt(7809 / 10) def test_compute_sigma0_sparse(): V = np.arange(0, 16) + 1 W_obs = np.diag(np.arange(16, 0, -1)) W_sensors = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) W_points = { "point_1" : np.array([[-1, -2, -3], [-4, -5, -6], [-7, -8, -9]]) } dX = np.arange(-6, 0) column_dict = { "image_1" : (0, 3), "point_1" : (3, 6) } assert bundle.compute_sigma_sparse(V, dX, W_sensors, W_points, W_obs, column_dict) == np.sqrt(7809 / 10)