def test_flux_reflected(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2, reflected=True)
theta = np.linspace(0, 30, 10)
xs = np.linspace(-1.5, 1.5, len(theta))
ys = np.ones_like(xs) * 0.3
zs = -1.0 * np.ones_like(xs)
ro = 0.0
inc = 85.0 * np.pi / 180.0
obl = 30.0 * np.pi / 180.0
y = np.ones(9)
u = [-1.0]
f = [np.pi]
Rs = 1.0
sigr = 30 * np.pi / 180
def func(theta, xs, ys, zs, Rs, ro, inc, obl, u, f):
return tt.dot(
map.ops.X(theta, xs, ys, zs, Rs, xs, ys, zs, ro, inc, obl, u,
f, sigr),
y,
)
# Just rotation
theano.gradient.verify_grad(
func,
(theta, xs, ys, zs, Rs, ro, inc, obl, u, f),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_tensordotRz(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2)
theta = (np.array([0.0, 15.0, 30.0, 45.0, 60.0, 75.0, 90.0]) * np.pi /
180.0)
# Matrix M
M = np.ones((7, 9))
theano.gradient.verify_grad(
map.ops.tensordotRz,
(M, theta),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
# Vector M
M = np.ones((1, 9))
theano.gradient.verify_grad(
map.ops.tensordotRz,
(M, theta),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_dotR(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2)
x = 1.0 / np.sqrt(3)
y = 1.0 / np.sqrt(3)
z = 1.0 / np.sqrt(3)
theta = np.pi / 5
# Matrix M
M = np.ones((7, 9))
theano.gradient.verify_grad(
map.ops.dotR,
(M, x, y, z, theta),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
# Vector M
M = np.ones((1, 9))
theano.gradient.verify_grad(
map.ops.dotR,
(M, x, y, z, theta),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_test_value_error():
"""
Ensure that no error is raised when there
are missing test values in our compiled
functions, as pymc3 forces theano errors when
test values are missing.
"""
with change_flags(compute_test_value="raise"):
map = starry.Map()
flux = map.flux()
def test_sT(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2)
theano.gradient.verify_grad(
map.ops.sT,
(np.linspace(0.01, 1.09, 30), 0.1),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_doppler_rT(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.DopplerMap(ydeg=5)
x = map.ops.get_x(np.array(1000.0))
theano.gradient.verify_grad(
map.ops.get_rT,
(x, ),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_rT_reflected(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2, reflected=True)
bterm = np.linspace(-1, 1, 10)[1:-1]
sigr = 30 * np.pi / 180
theano.gradient.verify_grad(
map.ops.rT,
(bterm, sigr),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_flux_ylm_ld(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2, udeg=2)
theta = np.linspace(0, 30, 10)
xo = np.linspace(-1.5, 1.5, len(theta))
yo = np.ones_like(xo) * 0.3
zo = 1.0 * np.ones_like(xo)
ro = 0.1
inc = 85.0 * np.pi / 180.0
obl = 30.0 * np.pi / 180.0
y = np.ones(9)
np.random.seed(14)
u = [-1.0] + list(np.random.randn(2))
f = [np.pi]
func = lambda *args: tt.dot(map.ops.X(*args), y)
# Just rotation
theano.gradient.verify_grad(
func,
(theta, xo, yo, zo, 0.0, inc, obl, u, f),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
# Just occultation
theano.gradient.verify_grad(
func,
(theta, xo / 3, yo, zo, ro, inc, obl, u, f),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
# Rotation + occultation
theano.gradient.verify_grad(
func,
(theta, xo, yo, zo, ro, inc, obl, u, f),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_F(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2, udeg=2, rv=True)
np.random.seed(11)
u = np.random.randn(3)
u[0] = -1
f = np.random.randn(16)
theano.gradient.verify_grad(
map.ops.F,
(u, f),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_sT_oblate(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(gdeg=4, oblate=True)
f = 0.2
theta = np.array([0.5, 0.5])
bo = np.array([0.75, 0.75])
ro = 0.5
theano.gradient.verify_grad(
map.ops.sT,
(f, theta, bo, ro),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_spot_spectral(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=5, nw=2)
amp = [-0.01, -0.02]
sigma = 0.1
lat = 30 * np.pi / 180
lon = 45 * np.pi / 180
theano.gradient.verify_grad(
map.ops.spotYlm,
(amp, sigma, lat, lon),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_pT(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2)
map[1:, :] = 1
x = np.array([0.13])
y = np.array([0.25])
z = np.sqrt(1 - x**2 - y**2)
theano.gradient.verify_grad(
map.ops.pT,
(x, y, z),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_rv(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2, rv=True)
theta = np.linspace(0, 30, 10)
xo = np.linspace(-1.5, 1.5, len(theta))
yo = np.ones_like(xo) * 0.3
zo = 1.0 * np.ones_like(xo)
ro = 0.1
inc = 85.0 * np.pi / 180.0
obl = 30.0 * np.pi / 180.0
veq = 0.5
alpha = 0.3
y = np.ones(9)
u = [-1.0]
# Just rotation
theano.gradient.verify_grad(
map.ops.rv,
(theta, xo, yo, zo, 0.0, inc, obl, y, u, veq, alpha),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
# Just occultation
theano.gradient.verify_grad(
map.ops.rv,
(theta, xo / 3, yo, zo, ro, inc, obl, y, u, veq, alpha),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
# Rotation + occultation
theano.gradient.verify_grad(
map.ops.rv,
(theta, xo, yo, zo, ro, inc, obl, y, u, veq, alpha),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_sT_reflected(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2, reflected=True)
b = np.array([0.5])
theta = np.array([0.5])
bo = np.array([0.75])
ro = 0.5
sigr = 30 * np.pi / 180
theano.gradient.verify_grad(
map.ops.sT,
(b, theta, bo, ro, sigr),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_flux_quad_ld(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(udeg=2)
xo = np.linspace(-1.5, 1.5, 10)
yo = np.ones_like(xo) * 0.3
zo = 1.0 * np.ones_like(xo)
ro = 0.1
np.random.seed(14)
u = np.array([-1.0] + list(np.random.randn(2)))
func = lambda *args: map.ops.flux(*args)
theano.gradient.verify_grad(
func,
(xo, yo, zo, ro, u),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_intensity(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2, udeg=2)
np.random.seed(11)
lat = 180 * (np.random.random(10) - 0.5)
lon = 360 * (np.random.random(10) - 0.5)
y = [1.0] + list(np.random.randn(8))
u = [-1.0] + list(np.random.randn(2))
f = [np.pi]
theta = 0.0
def intensity(lat, lon, y, u, f, theta):
return map.ops.intensity(lat, lon, y, u, f, theta, np.array(True))
theano.gradient.verify_grad(
intensity,
(lat, lon, y, u, f, theta),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
def test_zero_lat_lon(abs_tol=1e-5, rel_tol=1e-5, eps=1e-7):
"""
The gradient of the intensity is NaN when either the
latitude or the longitude is zero. There's a div by zero in
the Op that we need to work around.
"""
lats = [0.0001, 0.0, np.pi / 6, 0.0]
lons = [np.pi / 6, np.pi / 6, 0.0, 0.0]
for lat, lon in zip(lats, lons):
with change_flags(compute_test_value="off"):
map = starry.Map(ydeg=2, udeg=2)
np.random.seed(11)
y = np.array([1.0] + list(np.random.randn(8)))
u = np.array([-1.0] + list(np.random.randn(2)))
f = np.array([np.pi])
theta = np.array(0.0)
lat = np.array(lat)
lon = np.array(lon)
def intensity(lat, lon, y, u, f, theta):
return map.ops.intensity(
lat, lon, y, u, f, theta, np.array(True)
)
theano.gradient.verify_grad(
intensity,
(lat, lon, y, u, f, theta),
abs_tol=abs_tol,
rel_tol=rel_tol,
eps=eps,
n_tests=1,
rng=np.random,
)
