def _lnlike(r, a, b, c, n, i, p, t, flux, data_cov):
gp = StarryProcess(
r=r,
a=a,
b=b,
c=c,
n=n,
marginalize_over_inclination=marginalize_over_inclination,
normalized=False,
)
return gp.log_likelihood(t, flux, data_cov, p=p, i=i)
def test_profile_marg(gradient, profile, ydeg=15, npts=1000):
# Free parameters
r = tt.dscalar()
a = tt.dscalar()
b = tt.dscalar()
c = tt.dscalar()
n = tt.dscalar()
p = tt.dscalar()
t = tt.dvector()
flux = tt.dvector()
data_cov = tt.dscalar()
# Compute the mean and covariance
gp = StarryProcess(
r=r, a=a, b=b, c=c, n=n, marginalize_over_inclination=True
)
# Compile the function
if gradient:
g = lambda f, x: tt.grad(f, x)
else:
g = lambda f, x: f
func = theano.function(
[r, a, b, c, n, p, t, flux, data_cov],
[
g(gp.log_likelihood(t, flux, data_cov, p=p), a)
], # wrt a for definiteness
profile=profile,
)
# Run it
t = np.linspace(0, 1, npts)
flux = np.random.randn(npts)
data_cov = 1.0
run = lambda: func(
defaults["r"],
defaults["a"],
defaults["b"],
defaults["c"],
defaults["n"],
defaults["p"],
t,
flux,
data_cov,
)
if profile:
# Profile the full function
run()
print(func.profile.summary())
else:
# Time the execution
number = 100
time = timeit.timeit(run, number=number) / number
print("time elapsed: {:.4f} s".format(time))
if (gradient and time > 0.2) or (not gradient and time > 0.1):
warnings.warn("too slow! ({:.4f} s)".format(time))
def test_likelihood():
t = np.linspace(0, 1, 100)
flux = np.random.randn(100)
sp = StarryProcess(r=10) + StarryProcess(r=20)
ll = sp.log_likelihood(t, flux, 1.0).eval()
assert np.isfinite(ll)