__all__ = []
import time
import numpy as np
import matplotlib.pyplot as pl
from george import GaussianProcess
import bart
from bart.data import GPLightCurve
# Generate fake data.
gp = GaussianProcess([5e-4, 20.0, 25.0])
t = np.arange(100, 120, 0.5 / 24.0)
yerr = 1e-4 * np.ones_like(t)
y = gp.sample_prior(t)[0] + yerr * np.random.randn(len(yerr)) + 1
# Inject a transit.
t0_true = 110.0
planet = bart.Planet(r=0.02, period=400.0, t0=t0_true)
star = bart.Star(ldp=bart.ld.QuadraticLimbDarkening(0.3, 0.1))
ps = bart.PlanetarySystem(star)
ps.add_planet(planet)
model = ps.light_curve(t)
y *= model
# Plot the data.
pl.plot(t, y, ".k")
pl.savefig("data.png")
# Load the dataset using bart.
import numpy as np
import matplotlib.pyplot as pl
from george import GaussianProcess
from george.kernels import ExpSquaredKernel, CosineKernel
np.random.seed(123)
kernel = ExpSquaredKernel(1.0, 2.3)
gp = GaussianProcess(kernel)
# Generate some fake data.
period = 0.956
x = 10 * np.sort(np.random.rand(75))
yerr = 0.1 + 0.1 * np.random.rand(len(x))
y = gp.sample_prior(x)
y += 0.8 * np.cos(2 * np.pi * x / period)
y += yerr * np.random.randn(len(yerr))
# Set up a periodic kernel.
pk = ExpSquaredKernel(np.sqrt(0.8), 1000.0) * CosineKernel(period)
kernel2 = kernel + pk
gp2 = GaussianProcess(kernel2)
# Condition on this data.
gp2.compute(x, yerr)
# Compute the log-likelihood.
print("Log likelihood = {0}".format(gp2.lnlikelihood(y)))
# Compute the conditional predictive distribution.
