from unittest import mock import pytest import numpy as np import pandas as pd from knoten import bundle from collections import OrderedDict from csmapi import csmapi @pytest.fixture def control_network(): df_dict = { 'id': ['bob', 'bob', 'bob', 'tim', 'tim', 'sally', 'sally', 'sally', 'sally'], 'serialnumber': ['a', 'b', 'c', 'd', 'b', 'a', 'b', 'c', 'd'], 'line': np.arange(9), 'sample': np.arange(9)[::-1], 'aprioriCovar': [[], [], [], [], [], [], [], [], []], 'aprioriX': np.zeros(9), 'aprioriX': np.zeros(9), 'aprioriY': np.zeros(9), 'aprioriZ': np.zeros(9), 'adjustedX': np.zeros(9), 'adjustedY': np.zeros(9), 'adjustedZ': np.zeros(9), 'pointType': [2, 2, 2, 3, 3, 2, 2, 2, 2] } return pd.DataFrame(df_dict) @pytest.fixture def sensors(): sensors = { 'a': mock.MagicMock(spec=csmapi.RasterGM), 'b': mock.MagicMock(spec=csmapi.RasterGM), 'c': mock.MagicMock(spec=csmapi.RasterGM), 'd': mock.MagicMock(spec=csmapi.RasterGM) } return sensors def test_closest_approach_intersect(): points = np.array([[-1, 1, 2], [0, 2, 2], [0, 1, 3]]) directions = np.array([[1, 0, 0], [0, 2, 0], [0, 0, -1]]) res, covar = bundle.closest_approach(points, directions) np.testing.assert_allclose(res, [0, 1, 2]) def test_closest_approach_no_intersect(): points = np.array([[-1, 1, 2], [0.5, 1-np.sqrt(3)/2.0, 2], [0.5, 1+np.sqrt(3)/2.0, 4]]) directions = np.array([[0, 1, 0], [np.sqrt(3)/2.0, 0.5, 0], [0, 0, 1]]) res, covar = bundle.closest_approach(points, directions) np.testing.assert_allclose(res, [0, 1, 2], atol=1e-12) def test_compute_ground_points(control_network, sensors): expected_bob = np.array([1.0, 7.0, 0.0]) bob_covar = np.array([[1.0, 0.1, 0.2], [0.1, 1.5, 0.15], [0.2, 0.15, 3.0]]) expected_sally = np.array([6.5, 1.5, 0.0]) sally_covar = np.array([[2.0, 1.1, 0.6], [1.1, 1.0, 0.45], [0.6, 0.45, 3.2]]) with mock.patch('knoten.bundle.closest_approach', side_effect=[(expected_bob, bob_covar), (expected_sally, sally_covar)]) as mock_closest: out_df = bundle.compute_apriori_ground_points(control_network, sensors) mock_closest.assert_called() for _, row in out_df[out_df.id == "bob"].iterrows(): np.testing.assert_array_equal(row[["aprioriX", "aprioriY", "aprioriZ"]].values, expected_bob) np.testing.assert_array_equal(row[["adjustedX", "adjustedY", "adjustedZ"]].values, expected_bob) np.testing.assert_array_equal(list(row["aprioriCovar"]), [bob_covar[0,0], bob_covar[0,1], bob_covar[0,2], bob_covar[1,1], bob_covar[1,2], bob_covar[2,2]]) for _, row in out_df[out_df.id == "tim"].iterrows(): np.testing.assert_array_equal(row[["aprioriX", "aprioriY", "aprioriZ"]].values, np.zeros(3)) np.testing.assert_array_equal(row[["adjustedX", "adjustedY", "adjustedZ"]].values, np.zeros(3)) assert not list(row["aprioriCovar"]) for _, row in out_df[out_df.id == "sally"].iterrows(): np.testing.assert_array_equal(row[["aprioriX", "aprioriY", "aprioriZ"]].values, expected_sally) np.testing.assert_array_equal(row[["adjustedX", "adjustedY", "adjustedZ"]].values, expected_sally) np.testing.assert_array_equal(list(row["aprioriCovar"]), [sally_covar[0,0], sally_covar[0,1], sally_covar[0,2], sally_covar[1,1], sally_covar[1,2], sally_covar[2,2]]) def test_get_sensor_parameter(): mock_sensor = mock.MagicMock(spec=csmapi.RasterGM) mock_sensor.getParameterSetIndices.return_value = [0, 1, 2] parameters = bundle.get_sensor_parameters(mock_sensor) mock_sensor.getParameterSetIndices.assert_called() assert len(parameters) == 3 def test_csm_parameters(): mock_sensor = mock.MagicMock(spec=csmapi.RasterGM) test_parameter = bundle.CsmParameter(mock_sensor, 5) mock_sensor.getParameterName.assert_called_with(5) mock_sensor.getParameterType.assert_called_with(5) mock_sensor.getParameterUnits.assert_called_with(5) mock_sensor.getParameterValue.assert_called_with(5) assert test_parameter.index == 5 assert test_parameter.name == mock_sensor.getParameterName.return_value assert test_parameter.type == mock_sensor.getParameterType.return_value assert test_parameter.units == mock_sensor.getParameterUnits.return_value assert test_parameter.value == mock_sensor.getParameterValue.return_value def test_compute_sensor_partials(): ground_pt = [9, 8, 10] sensor = mock.MagicMock(spec=csmapi.RasterGM) sensor.computeSensorPartials.side_effect = [(5, 3), (4, 2), (6, 1)] parameters = [mock.MagicMock(), mock.MagicMock(), mock.MagicMock()] partials = bundle.compute_sensor_partials(sensor, parameters, ground_pt) np.testing.assert_array_equal(partials, [(5, 4, 6), (3, 2, 1)]) def test_compute_ground_partials(): ground_pt = [9, 8, 10] sensor = mock.MagicMock(spec=csmapi.RasterGM) sensor.computeGroundPartials.return_value = (1, 2, 3, 4, 5, 6) partials = bundle.compute_ground_partials(sensor, ground_pt) np.testing.assert_array_equal(partials, [[1, 2, 3], [4, 5, 6]]) def test_compute_jacobian(control_network, sensors): parameters = {sn: [mock.MagicMock()]*2 for sn in sensors} sensor_partials = [(i+1) * np.ones((2, 2)) for i in range(9)] ground_partials = [-(i+1) * np.ones((2, 3)) for i in range(9)] coefficient_columns = OrderedDict() coefficient_columns['a'] = (0, 2) coefficient_columns['b'] = (2, 4) coefficient_columns['c'] = (4, 6) coefficient_columns['d'] = (6, 8) coefficient_columns['bob'] = (8, 11) coefficient_columns['tim'] = (11, 14) coefficient_columns['sally'] = (14, 17) with mock.patch('knoten.bundle.compute_sensor_partials', side_effect=sensor_partials) as sensor_par_mock, \ mock.patch('knoten.bundle.compute_ground_partials', side_effect=ground_partials) as ground_par_mock: J = bundle.compute_jacobian(control_network, sensors, parameters, coefficient_columns) expected_J = [ [1, 1, 0, 0, 0, 0, 0, 0, -1, -1, -1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, -1, -1, -1, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0, 0, 0, -2, -2, -2, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0, 0, 0, -2, -2, -2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, -3, -3, -3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, -3, -3, -3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, -4, -4, -4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, -4, -4, -4, 0, 0, 0], [0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, -5, -5, -5, 0, 0, 0], [0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, -5, -5, -5, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6, -6, -6], [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6, -6, -6], [0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -7, -7, -7], [0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -7, -7, -7], [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, -8, -8, -8], [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, -8, -8, -8], [0, 0, 0, 0, 0, 0, 9, 9, 0, 0, 0, 0, 0, 0, -9, -9, -9], [0, 0, 0, 0, 0, 0, 9, 9, 0, 0, 0, 0, 0, 0, -9, -9, -9]] np.testing.assert_array_equal(J, expected_J) def test_compute_residuals(control_network, sensors): # sensor.groundToImage.side_effect = [csmapi.ImageCoord(-0.1, 7.8), csmapi.ImageCoord(0.7, 6.6), csmapi.ImageCoord(1.5, 5.4), # csmapi.ImageCoord(2.3, 4.2), csmapi.ImageCoord(3.1, 4.9), csmapi.ImageCoord(5.8, 3.7), # csmapi.ImageCoord(6.6, 2.5), csmapi.ImageCoord(7.4, 1.3), csmapi.ImageCoord(8.2, 0.1)] sensors['a'].groundToImage.side_effect = [csmapi.ImageCoord(-0.1, 7.8), csmapi.ImageCoord(5.8, 3.7)] sensors['b'].groundToImage.side_effect = [csmapi.ImageCoord(0.7, 6.6), csmapi.ImageCoord(3.1, 4.9), csmapi.ImageCoord(6.6, 2.5)] sensors['c'].groundToImage.side_effect = [csmapi.ImageCoord(1.5, 5.4), csmapi.ImageCoord(7.4, 1.3)] sensors['d'].groundToImage.side_effect = [csmapi.ImageCoord(2.3, 4.2), csmapi.ImageCoord(8.2, 0.1)] 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])