def _sample(r, a, b, c, n, i, p, t):
gp = StarryProcess(
r=r,
a=a,
b=b,
c=c,
n=n,
marginalize_over_inclination=marginalize_over_inclination,
normalized=False,
)
gp.random.seed(seed)
return tt.reshape(gp.sample(t, p=p, i=i), (-1,))
def test_sample_conditional():
# Sample the flux from the prior
sp = StarryProcess(normalized=False, marginalize_over_inclination=False)
t = np.linspace(0, 2, 300)
flux = sp.sample(t, p=1.0, i=60.0).eval().reshape(-1)
# Now sample the ylms conditioned on the flux
data_cov = 1e-6
y = sp.sample_ylm_conditional(t, flux, data_cov, p=1.0, i=60.0).eval()
map = starry.Map(15, inc=60, lazy=False)
map[:, :] = y.reshape(-1)
flux_pred = map.flux(theta=360 * t)
# The computed flux should match the data pretty well
chisq = np.sum((flux - flux_pred)**2 / data_cov)
assert chisq / len(t) < 1