def test_amb_from_baseline():
N_true = np.array([22, 23, 34, -123])
DE = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 1]])
b = np.array([1, 1, 1])
dd_obs = bl.predict_carrier_obs_(N_true, DE, b)
N = bl.amb_from_baseline_(DE, dd_obs, b)
assert np.allclose(N, N_true)
def test_predict_carrier_obs2(): N = np.array([222.2]) DE = np.matrix([[3, 4, 5]]) b = np.array([7, 8, 9]) dd_obs_expected = np.array([(3*7 + 4*8 + 5*9)/0.19023800915688557 + 222.2]) dd_obs = bl.predict_carrier_obs_(N, DE, b) assert np.allclose(dd_obs, dd_obs_expected)
def test_lesq_solution2():
# Exactly constrained.
N = np.array([22, 23, 34])
DE = np.matrix([[1, 0, 0], [0, 1, 0], [1, 1, 1]])
b_true = np.array([1.234, 1.456, 1.789])
dd_obs = bl.predict_carrier_obs_(N, DE, b_true)
code, b, resid = bl.lesq_solution_float_(dd_obs, N, DE)
assert code == 0
assert np.allclose(b, b_true)
def test_predict_carrier_obs2():
N = np.array([222.2])
DE = np.matrix([[3, 4, 5]])
b = np.array([7, 8, 9])
dd_obs_expected = np.array([
(3 * 7 + 4 * 8 + 5 * 9) / 0.19023800915688557 + 222.2
])
dd_obs = bl.predict_carrier_obs_(N, DE, b)
assert np.allclose(dd_obs, dd_obs_expected)
def test_predict_carrier_obs():
N = np.array([22.2, 23.3, 34.4, 123.4])
DE = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 1]])
b = np.array([1, 1, 1])
dd_obs = bl.predict_carrier_obs_(N, DE, b)
dd_obs_expected = np.array([
1.0 / 0.19023800915688557 + 22.2, 1.0 / 0.19023800915688557 + 23.3,
1.0 / 0.19023800915688557 + 34.4, 3.0 / 0.19023800915688557 + 123.4
])
assert np.allclose(dd_obs, dd_obs_expected)
def test_amb_from_baseline():
N_true = np.array([22, 23, 34, -123])
DE = np.matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[1, 1, 1]])
b = np.array([1, 1, 1])
dd_obs = bl.predict_carrier_obs_(N_true, DE, b)
N = bl.amb_from_baseline_(DE, dd_obs, b)
assert np.allclose(N, N_true)
def test_lesq_solution2():
# Exactly constrained.
N = np.array([22, 23, 34])
DE = np.matrix([[1, 0, 0],
[0, 1, 0],
[1, 1, 1]])
b_true = np.array([1.234, 1.456, 1.789])
dd_obs = bl.predict_carrier_obs_(N, DE, b_true)
code, b, resid = bl.lesq_solution_float_(dd_obs, N, DE)
assert code == 0
assert np.allclose(b, b_true)
def test_predict_carrier_obs():
N = np.array([22.2, 23.3, 34.4, 123.4])
DE = np.matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[1, 1, 1]])
b = np.array([1, 1, 1])
dd_obs = bl.predict_carrier_obs_(N, DE, b)
dd_obs_expected = np.array([1.0/0.19023800915688557 + 22.2,
1.0/0.19023800915688557 + 23.3,
1.0/0.19023800915688557 + 34.4,
3.0/0.19023800915688557 + 123.4])
assert np.allclose(dd_obs, dd_obs_expected)
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_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)
