def test_fit_core():
'''
creating some great circles pointing in different directions (two
north-south,
two east-west) and that have a slight position errors (+- 0.1 m in one of
the four
cardinal directions '''
circle1 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle1.pos = [0, 0.1] * u.m
circle1.trace = [1, 0, 0]
circle2 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle2.pos = [0.1, 0] * u.m
circle2.trace = [0, 1, 0]
circle3 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle3.pos = [0, -.1] * u.m
circle3.trace = [1, 0, 0]
circle4 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle4.pos = [-.1, 0] * u.m
circle4.trace = [0, 1, 0]
# creating the fit class and setting the the great circle member
fit = HillasReconstructor()
fit.circles = {1: circle1, 2: circle2, 3: circle3, 4: circle4}
# performing the position fit with the minimisation algorithm
# and a seed that is quite far away
pos_fit_minimise = fit.fit_core_minimise([100, 1000] * u.m)
print("position fit test minimise:", pos_fit_minimise)
print()
# performing the position fit with the geometric algorithm
pos_fit_crosses, err_est_pos_fit_crosses = fit.fit_core_crosses()
print("position fit test crosses:", pos_fit_crosses)
print("error estimate:", err_est_pos_fit_crosses)
print()
# the results should be close to the origin of the coordinate system
np.testing.assert_allclose(pos_fit_minimise / u.m, [0, 0], atol=1e-3)
np.testing.assert_allclose(pos_fit_crosses / u.m, [0, 0], atol=1e-3)
def test_fit_core():
'''
creating some great circles pointing in different directions (two
north-south,
two east-west) and that have a slight position errors (+- 0.1 m in one of
the four
cardinal directions '''
circle1 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle1.pos = [0, 0.1] * u.m
circle1.trace = [1, 0, 0]
circle2 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle2.pos = [0.1, 0] * u.m
circle2.trace = [0, 1, 0]
circle3 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle3.pos = [0, -.1] * u.m
circle3.trace = [1, 0, 0]
circle4 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle4.pos = [-.1, 0] * u.m
circle4.trace = [0, 1, 0]
# creating the fit class and setting the the great circle member
fit = HillasReconstructor()
fit.circles = {1: circle1, 2: circle2, 3: circle3, 4: circle4}
# performing the position fit with the minimisation algorithm
# and a seed that is quite far away
pos_fit_minimise = fit.fit_core_minimise([100, 1000] * u.m)
print("position fit test minimise:", pos_fit_minimise)
print()
# performing the position fit with the geometric algorithm
pos_fit_crosses, err_est_pos_fit_crosses = fit.fit_core_crosses()
print("position fit test crosses:", pos_fit_crosses)
print("error estimate:", err_est_pos_fit_crosses)
print()
# the results should be close to the origin of the coordinate system
np.testing.assert_allclose(pos_fit_minimise / u.m, [0, 0], atol=1e-3)
np.testing.assert_allclose(pos_fit_crosses / u.m, [0, 0], atol=1e-3)
def test_fit_origin():
'''
creating some great circles pointing in different directions (two
north-south,
two east-west) and that have a slight position errors (+- 0.1 m in one of
the four
cardinal directions '''
circle1 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle1.pos = [0, 0.1] * u.m
circle1.trace = [1, 0, 0]
circle2 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle2.pos = [0.1, 0] * u.m
circle2.trace = [0, 1, 0]
circle3 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle3.pos = [0, -.1] * u.m
circle3.trace = [1, 0, 0]
circle4 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle4.pos = [-.1, 0] * u.m
circle4.trace = [0, 1, 0]
# creating the fit class and setting the the great circle member
fit = HillasReconstructor()
fit.circles = {1: circle1, 2: circle2, 3: circle3, 4: circle4}
# performing the direction fit with the minimisation algorithm
# and a seed that is perpendicular to the up direction
dir_fit_minimise = fit.fit_origin_minimise((0.1, 0.1, 1))
print("direction fit test minimise:", dir_fit_minimise)
print()
# performing the direction fit with the geometric algorithm
dir_fit_crosses = fit.fit_origin_crosses()[0]
print("direction fit test crosses:", dir_fit_crosses)
print()
# the results should be close to the direction straight up
# np.testing.assert_allclose(dir_fit_minimise, [0, 0, 1], atol=1e-1)
np.testing.assert_allclose(dir_fit_crosses, [0, 0, 1], atol=1e-3)
def test_fit_origin():
'''
creating some great circles pointing in different directions (two
north-south,
two east-west) and that have a slight position errors (+- 0.1 m in one of
the four
cardinal directions '''
circle1 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle1.pos = [0, 0.1] * u.m
circle1.trace = [1, 0, 0]
circle2 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle2.pos = [0.1, 0] * u.m
circle2.trace = [0, 1, 0]
circle3 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle3.pos = [0, -.1] * u.m
circle3.trace = [1, 0, 0]
circle4 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle4.pos = [-.1, 0] * u.m
circle4.trace = [0, 1, 0]
# creating the fit class and setting the the great circle member
fit = HillasReconstructor()
fit.circles = {1: circle1, 2: circle2, 3: circle3, 4: circle4}
# performing the direction fit with the minimisation algorithm
# and a seed that is perpendicular to the up direction
dir_fit_minimise = fit.fit_origin_minimise((0.1, 0.1, 1))
print("direction fit test minimise:", dir_fit_minimise)
print()
# performing the direction fit with the geometric algorithm
dir_fit_crosses = fit.fit_origin_crosses()[0]
print("direction fit test crosses:", dir_fit_crosses)
print()
# the results should be close to the direction straight up
# np.testing.assert_allclose(dir_fit_minimise, [0, 0, 1], atol=1e-1)
np.testing.assert_allclose(dir_fit_crosses, [0, 0, 1], atol=1e-3)