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db_connector.py
test_bundle.py 8.52 KiB
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])