def lnlike_gp(self):
if (self.t is None) | (self.y is None):
raise ValueError(
"Data is not properly initialized. Reveived Nones.")
elif len(self.t) == 1:
raise ValueError(
"Time data is not properly initialized. Expected array of size greater then 1."
)
else:
t, y = self.t, self.y
if self.kernel_type == "Standard":
kernel = 1. * kernels.ExpSquaredKernel(
5.) + kernels.WhiteKernel(2.)
gp = george.GP(kernel, mean=self.meanfnc)
gp.compute(t, self.yerr1)
return gp.lnlikelihood(y - self.model)
else:
kernel = kernels.PythonKernel(self.kernelfnc)
gp = george.GP(kernel, mean=self.meanfnc)
gp.compute(t)
return gp.lnlikelihood(y - self.model)
def angus_kernel(theta):
"""
use the kernel that Ruth Angus uses. Be sure to cite her
"""
theta = np.exp(theta)
A = theta[0]
l = theta[1]
G = theta[2]
sigma = theta[4]
P = theta[3]
kernel = (A * kernels.ExpSquaredKernel(l) *
kernels.ExpSine2Kernel(G, P) +
kernels.WhiteKernel(sigma)
)
return kernel
fig = pl.figure(figsize=(6, 3.5))
ax = fig.add_subplot(111)
ax.plot(t, y, ".k", ms=2)
ax.set_xlim(min(t), 1999)
ax.set_ylim(min(y), 369)
ax.set_xlabel("year")
ax.set_ylabel("CO$_2$ in ppm")
fig.subplots_adjust(left=0.15, bottom=0.2, right=0.99, top=0.95)
fig.savefig("../_static/hyper/data.png", dpi=150)
# Initialize the kernel.
k1 = 66.0**2 * kernels.ExpSquaredKernel(67.0**2)
k2 = 2.4**2 * kernels.ExpSquaredKernel(90**2) \
* kernels.ExpSine2Kernel(2.0 / 1.3**2, 1.0)
k3 = 0.66**2 * kernels.RationalQuadraticKernel(0.78, 1.2**2)
k4 = 0.18**2 * kernels.ExpSquaredKernel(1.6**2) + kernels.WhiteKernel(0.19)
kernel = k1 + k2 + k3 + k4
# Set up the Gaussian process and maximize the marginalized likelihood.
gp = george.GP(kernel, mean=np.mean(y))
# Define the objective function (negative log-likelihood in this case).
def nll(p):
# Update the kernel parameters and compute the likelihood.
gp.kernel[:] = p
ll = gp.lnlikelihood(y, quiet=True)
# The scipy optimizer doesn't play well with infinities.
return -ll if np.isfinite(ll) else 1e25
# And the gradient of the objective function.
def main():
k1 = 66.0**2 * kernels.ExpSquaredKernel(67.0**2)
k2 = 2.4**2 * kernels.ExpSquaredKernel(90**2) * kernels.ExpSine2Kernel(
2.0 / 1.3**2, 1.0)
k3 = 0.66**2 * kernels.RationalQuadraticKernel(0.78, 1.2**2)
k4 = 0.18**2 * kernels.ExpSquaredKernel(1.6**2) + kernels.WhiteKernel(0.19)
kernel = k2 + k4 #k1 + k2 + k3 + k4
gp = george.GP(kernel)
indata = np.loadtxt(
'/Users/lapguest/newbol/bol_ni_ej/out_files/err_bivar_regress.txt',
usecols=(5, 6, 1, 2),
skiprows=1)
def nll(p):
# Update the kernel parameters and compute the likelihood.
gp.kernel[:] = p
ll = gp.lnlikelihood(indata[:, 2], quiet=True)
# The scipy optimizer doesn't play well with infinities.
return -ll if np.isfinite(ll) else 1e25
# And the gradient of the objective function.
def grad_nll(p):
# Update the kernel parameters and compute the likelihood.
gp.kernel[:] = p
return -gp.grad_lnlikelihood(indata[:, 2], quiet=True)
#ph=lc['MJD']-tbmax
#condition for second maximum
##TODO: GUI for selecting region
#cond=(ph>=10.0) & (ph<=40.0)
#define the data in the region of interest
print "Fitting with george"
#print max(mag)
# Pre-compute the factorization of the matrix.
gp.compute(indata[:, 0], indata[:, 2])
#print gp.lnlikelihood(mag), gp.grad_lnlikelihood(mag)
gp.compute(indata[:, 0], indata[:, 2])
if sys.argv[1] == 'mle':
p0 = gp.kernel.vector
results = op.minimize(nll, p0, jac=grad_nll)
gp.kernel[:] = results.x
print gp.kernel.value
t2 = indata[:, 0]
t = np.linspace(t2.min(), t2.max(), 100)
mu, cov = gp.predict(indata[:, 1], t)
print gp.predict(indata[:, 1], 31.99)[0]
std = np.sqrt(np.diag(cov))
plt.plot(t2, indata[:, 2], 'bo')
plt.plot(t, mu, 'r:', linewidth=3)
plt.fill_between(t, mu - std, mu + std, alpha=0.3)
plt.show()