def test_lesq_repair2():
# Bad DE row, not enough rows to repair.
N = np.array([0, 0, 0, 0])
DE = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1], [22, 222, 222]])
dd_obs = np.array([1, 1, 1, 1])
code, num_used, residuals, removed_obs, b \
= bl.lesq_solve_raim_(dd_obs, N, DE, False, bl.DEFAULT_RAIM_THRESHOLD_)
assert np.allclose(b, np.array([0.1705906, -0.00802221, -0.00802221]))
assert num_used == 0
assert np.allclose(residuals, np.zeros(num_used))
assert removed_obs == 0
assert code == -4, "Expecting -4 for not enough dds to repair, got: %i." % code
def test_lesq_repair1():
# Test raim repair: over constrained with bad DE row.
N = np.array([0, 0, 0, 0, 0])
DE = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 1], [22, 222,
222]])
dd_obs = np.array([1, 1, 1, 3, 1])
code, num_used, residuals, removed_obs, b \
= bl.lesq_solve_raim_(dd_obs, N, DE, False, bl.DEFAULT_RAIM_THRESHOLD_)
assert num_used == 4
assert np.allclose(residuals, np.zeros(num_used))
assert removed_obs == 4
assert np.allclose(b, np.array([0.19023801, 0.19023801, 0.19023801]))
assert code == 1, "Expecting 1 for repaired solution, got: %i." % code
assert removed_obs == 4, \
"Expecting repaired solution (dropping index 4 of DE), got: %i." % removed_obs
def test_lesq_repair2():
# Bad DE row, not enough rows to repair.
N = np.array([0, 0, 0, 0])
DE = np.matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[22, 222, 222]])
dd_obs = np.array([1, 1, 1, 1])
code, num_used, residuals, removed_obs, b \
= bl.lesq_solve_raim_(dd_obs, N, DE, False, bl.DEFAULT_RAIM_THRESHOLD_)
assert np.allclose(b, np.array([ 0.1705906, -0.00802221, -0.00802221]))
assert num_used == 0
assert np.allclose(residuals, np.zeros(num_used))
assert removed_obs == 0
assert code == -4, "Expecting -4 for not enough dds to repair, got: %i." % code
def test_lesq_repair_disabled():
# Test raim disabling flag: Over constrained with bad DE row.
N = np.array([0, 0, 0, 0, 0])
DE = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 1], [22, 222,
222]])
dd_obs = np.array([1, 1, 1, 3, 1])
# DISABLE raim
code, num_used, residuals, removed_obs, b \
= bl.lesq_solve_raim_(dd_obs, N, DE, True, bl.DEFAULT_RAIM_THRESHOLD_)
assert np.allclose(b, np.array([0.37698481, -0.01824651, -0.01824651]))
assert num_used == 5
assert np.allclose(
residuals,
np.array([-0.9816482, 1.09591413, 1.09591413, 1.21018006,
-0.01038781]))
assert removed_obs == 0
assert code == 2, "Expecting 1 for repaired solution, got: %i." % code
def test_lesq_solution4():
# Over constrained, integer valued ambiguity
N = np.array([22, 23, 34, -123])
DE = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 1]])
b_true = np.array([1.234, 1.456, 1.789])
dd_obs = bl.predict_carrier_obs_(N, DE, b_true)
N_int = N
code, num_used, residuals, removed_obs, b \
= bl.lesq_solve_raim_(dd_obs, N_int, DE, False, bl.DEFAULT_RAIM_THRESHOLD_)
assert num_used == 4
assert np.allclose(
residuals,
np.array(
[1.77635684e-15, -3.55271368e-15, -5.32907052e-15,
3.55271368e-15]))
assert removed_obs == 0
assert np.allclose(b, np.array([1.234, 1.456, 1.789]))
assert np.allclose(b, b_true)
def test_lesq_repair_disabled():
# Test raim disabling flag: Over constrained with bad DE row.
N = np.array([0, 0, 0, 0, 0])
DE = np.matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[1, 1, 1],
[22, 222, 222]])
dd_obs = np.array([1, 1, 1, 3, 1])
# DISABLE raim
code, num_used, residuals, removed_obs, b \
= bl.lesq_solve_raim_(dd_obs, N, DE, True, bl.DEFAULT_RAIM_THRESHOLD_)
assert np.allclose(b, np.array([ 0.37698481, -0.01824651, -0.01824651]))
assert num_used == 5
assert np.allclose(residuals,
np.array([-0.9816482, 1.09591413, 1.09591413, 1.21018006, -0.01038781]))
assert removed_obs == 0
assert code == 2, "Expecting 1 for repaired solution, got: %i." % code
def test_lesq_repair1():
# Test raim repair: over constrained with bad DE row.
N = np.array([0, 0, 0, 0, 0])
DE = np.matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[1, 1, 1],
[22, 222, 222]])
dd_obs = np.array([1, 1, 1, 3, 1])
code, num_used, residuals, removed_obs, b \
= bl.lesq_solve_raim_(dd_obs, N, DE, False, bl.DEFAULT_RAIM_THRESHOLD_)
assert num_used == 4
assert np.allclose(residuals, np.zeros(num_used))
assert removed_obs == 4
assert np.allclose(b, np.array([ 0.19023801, 0.19023801, 0.19023801]))
assert code == 1, "Expecting 1 for repaired solution, got: %i." % code
assert removed_obs == 4, \
"Expecting repaired solution (dropping index 4 of DE), got: %i." % removed_obs
def test_lesq_solution4():
# Over constrained, integer valued ambiguity
N = np.array([22, 23, 34, -123])
DE = np.matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[1, 1, 1]])
b_true = np.array([1.234, 1.456, 1.789])
dd_obs = bl.predict_carrier_obs_(N, DE, b_true)
N_int = N
code, num_used, residuals, removed_obs, b \
= bl.lesq_solve_raim_(dd_obs, N_int, DE, False, bl.DEFAULT_RAIM_THRESHOLD_)
assert num_used == 4
assert np.allclose(residuals,
np.array([ 1.77635684e-15, -3.55271368e-15,
-5.32907052e-15, 3.55271368e-15]))
assert removed_obs == 0
assert np.allclose(b, np.array([ 1.234, 1.456, 1.789]))
assert np.allclose(b, b_true)