def test_fit_GP_SNRefsdal_all_lc(self):
# k = gptools.SquaredExponentialKernel(param_bounds=[(0, 1e3), (0, 100)])
# gp = gptools.GaussianProcess(k, mu=gptools.LinearMeanFunction())
# add data
dm = -29.38 # D = 7.5e6 pc
# dm = -30.4 # D = 12.e6 pc
image = "S1"
bands = ['F160W', 'F105W', 'F125W']
# bands = ('F160W','F140W','F105W', 'F125W')
curves = snrefsdal.read_curves(snrefsdal.path_data, image)
for bname in bands:
lc = curves.get(bname)
# lc.mshift = dm
t = lc.Time
y = lc.Mag
yerr = lc.MagErr
# Gaussian process
k = gptools.SquaredExponentialKernel(param_bounds=[(min(np.abs(y)), max(np.abs(y))),
(0, np.std(t))])
# k = gptools.SquaredExponentialKernel(param_bounds=[(min(np.abs(y)), max(np.abs(y))),
# (0, np.std(t))])
gp = gptools.GaussianProcess(k)
# gp = gptools.GaussianProcess(k, mu=gptools.LinearMeanFunction())
gp.add_data(t, y, err_y=yerr)
is_mcmc = True
if is_mcmc:
out = gp.predict(t, use_MCMC=True, full_MCMC=True, return_std=True,
num_proc=0, nsamp=100,
plot_posterior=True, plot_chains=False,
burn=10, thin=1)
else:
gp.optimize_hyperparameters()
out = gp.predict(t, use_MCMC=False)
y_star, err_y_star = out
# y_star, err_y_star = gp.predict(t)
fig = plt.figure()
ax = fig.add_axes((0.1, 0.3, 0.8, 0.65))
ax.invert_yaxis()
bcolor = band.bands_colors()[bname]
ax.plot(t, y, color=bcolor, label='L bol', lw=2.5)
ax.errorbar(t, y, yerr=yerr, fmt='o', color=bcolor, label='%s obs.')
#
# ax.plot(t, y_star, color='red', ls='--', lw=1.5, label='GP')
ax.plot(t, y_star, '-', color=bcolor)
ax.fill_between(t, y_star - 2 * err_y_star, y_star + 2 * err_y_star, color=bcolor, alpha=0.3)
plt.show()
def test_fit_GP_SNRefsdal_all_lc(self):
# k = gptools.SquaredExponentialKernel(param_bounds=[(0, 1e3), (0, 100)])
# gp = gptools.GaussianProcess(k, mu=gptools.LinearMeanFunction())
# add data
dm = -29.38 # D = 7.5e6 pc
# dm = -30.4 # D = 12.e6 pc
image = "S1"
bands = ['F160W', 'F105W', 'F125W']
# bands = ('F160W','F140W','F105W', 'F125W')
curves = snrefsdal.read_curves(snrefsdal.path_data, image)
for bname in bands:
lc = curves.get(bname)
# lc.mshift = dm
t = lc.Time
y = lc.Mag
yerr = lc.Err
# Gaussian process
k = gptools.SquaredExponentialKernel(param_bounds=[(min(np.abs(y)), max(np.abs(y))),
(0, np.std(t))])
# k = gptools.SquaredExponentialKernel(param_bounds=[(min(np.abs(y)), max(np.abs(y))),
# (0, np.std(t))])
gp = gptools.GaussianProcess(k)
# gp = gptools.GaussianProcess(k, mu=gptools.LinearMeanFunction())
gp.add_data(t, y, err_y=yerr)
is_mcmc = True
if is_mcmc:
out = gp.predict(t, use_MCMC=True, full_MCMC=True, return_std=True,
num_proc=0, nsamp=100,
plot_posterior=True, plot_chains=False,
burn=10, thin=1)
else:
gp.optimize_hyperparameters()
out = gp.predict(t, use_MCMC=False)
y_star, err_y_star = out
# y_star, err_y_star = gp.predict(t)
fig = plt.figure()
ax = fig.add_axes((0.1, 0.3, 0.8, 0.65))
ax.invert_yaxis()
bcolor = band.colors()[bname]
ax.plot(t, y, color=bcolor, label='L bol', lw=2.5)
ax.errorbar(t, y, yerr=yerr, fmt='o', color=bcolor, label='%s obs.')
#
# ax.plot(t, y_star, color='red', ls='--', lw=1.5, label='GP')
ax.plot(t, y_star, '-', color=bcolor)
ax.fill_between(t, y_star - 2 * err_y_star, y_star + 2 * err_y_star, color=bcolor, alpha=0.3)
plt.show()
def test_fit_GP_SNRefsdal(self):
# k = gptools.SquaredExponentialKernel(param_bounds=[(0, 1e3), (0, 100)])
# gp = gptools.GaussianProcess(k, mu=gptools.LinearMeanFunction())
# add data
dm = -29.38 # D = 7.5e6 pc
# dm = -30.4 # D = 12.e6 pc
image = "S1"
bname = 'F160W'
curves = snrefsdal.read_curves(snrefsdal.path_data, image)
lc = curves.get(bname)
# lc.mshift = dm
t = lc.Time
y = lc.Mag
yerr = lc.MagErr
# Gaussian process
k = gptools.SquaredExponentialKernel(param_bounds=[(0, max(np.abs(y))),
(0, np.std(t))])
# k = gptools.SquaredExponentialKernel(param_bounds=[(min(np.abs(y)), max(np.abs(y))),
# (0, np.std(t))])
gp = gptools.GaussianProcess(k)
# gp = gptools.GaussianProcess(k, mu=gptools.LinearMeanFunction())
gp.add_data(t, y, err_y=yerr)
gp.optimize_hyperparameters()
y_star, err_y_star = gp.predict(t)
fig = plt.figure()
ax = fig.add_axes((0.1, 0.3, 0.8, 0.65))
ax.invert_yaxis()
ax.plot(t, y, color='blue', label='L bol', lw=2.5)
ax.errorbar(t, y, yerr=yerr, fmt='o', color='blue', label='%s obs.')
#
# ax.plot(t, y_star, color='red', ls='--', lw=1.5, label='GP')
ax.plot(t, y_star, '-', color='gray')
ax.fill_between(t, y_star - 2 * err_y_star, y_star + 2 * err_y_star, color='gray', alpha=0.3)
plt.show()
def test_fit_GP_SNRefsdal(self):
# k = gptools.SquaredExponentialKernel(param_bounds=[(0, 1e3), (0, 100)])
# gp = gptools.GaussianProcess(k, mu=gptools.LinearMeanFunction())
# add data
dm = -29.38 # D = 7.5e6 pc
# dm = -30.4 # D = 12.e6 pc
image = "S1"
bname = 'F160W'
curves = snrefsdal.read_curves(snrefsdal.path_data, image)
lc = curves.get(bname)
# lc.mshift = dm
t = lc.Time
y = lc.Mag
yerr = lc.Err
# Gaussian process
k = gptools.SquaredExponentialKernel(param_bounds=[(0, max(np.abs(y))),
(0, np.std(t))])
# k = gptools.SquaredExponentialKernel(param_bounds=[(min(np.abs(y)), max(np.abs(y))),
# (0, np.std(t))])
gp = gptools.GaussianProcess(k)
# gp = gptools.GaussianProcess(k, mu=gptools.LinearMeanFunction())
gp.add_data(t, y, err_y=yerr)
gp.optimize_hyperparameters()
y_star, err_y_star = gp.predict(t)
fig = plt.figure()
ax = fig.add_axes((0.1, 0.3, 0.8, 0.65))
ax.invert_yaxis()
ax.plot(t, y, color='blue', label='L bol', lw=2.5)
ax.errorbar(t, y, yerr=yerr, fmt='o', color='blue', label='%s obs.')
#
# ax.plot(t, y_star, color='red', ls='--', lw=1.5, label='GP')
ax.plot(t, y_star, '-', color='gray')
ax.fill_between(t, y_star - 2 * err_y_star, y_star + 2 * err_y_star, color='gray', alpha=0.3)
plt.show()
def test_scikit_GP_SNRefsdal(self):
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
# add data
dm = -29.38 # D = 7.5e6 pc
# dm = -30.4 # D = 12.e6 pc
image = "S1"
bname = 'F160W'
curves = snrefsdal.read_curves(snrefsdal.path_data, image)
lc = curves.get(bname)
# lc.mshift = dm
t = lc.Time
y = lc.Mag
yerr = lc.MagErr
#
# Instanciate a Gaussian Process model
# kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
# Instanciate a Gaussian Process model
# kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
# gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)
gp = GaussianProcessRegressor()
# Fit to data using Maximum Likelihood Estimation of the parameters
X = np.atleast_2d(t).T
gp.fit(X, y)
# gp = GaussianProcessRegressor()
# Fit to data using Maximum Likelihood Estimation of the parameters
# gp.fit(t, y)
# Make the prediction on the meshed x-axis (ask for MSE as well)
# y_star, err_y_star = gp.predict(t, return_std=True)
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, sigma = gp.predict(t, return_std=True)
# k = gptools.SquaredExponentialKernel(param_bounds=[(min(np.abs(y)), max(np.abs(y))),
# (0, np.std(t))])
# # k = gptools.SquaredExponentialKernel(param_bounds=[(min(np.abs(y)), max(np.abs(y))),
# # (0, np.std(t))])
# gp = gptools.GaussianProcess(k)
# # gp = gptools.GaussianProcess(k, mu=gptools.LinearMeanFunction())
# gp.add_data(t, y, err_y=yerr)
#
# gp.optimize_hyperparameters()
# y_star, err_y_star = gp.predict(t)
fig = plt.figure()
ax = fig.add_axes((0.1, 0.3, 0.8, 0.65))
ax.invert_yaxis()
ax.plot(t, y, color='blue', label='L bol', lw=2.5)
ax.errorbar(t, y, yerr=yerr, fmt='o', color='blue', label='%s obs.')
#
# ax.plot(t, y_star, color='red', ls='--', lw=1.5, label='GP')
ax.plot(t, y_pred, '-', color='gray')
# ax.fill_between(t, y_star - 2 * err_y_star, y_star + 2 * err_y_star, color='gray', alpha=0.3)
ax.fill(np.concatenate([t, t[::-1]]),
np.concatenate([y_pred - 1.9600 * sigma,
(y_pred + 1.9600 * sigma)[::-1]]),
alpha=.5, fc='b', ec='None', label='95% confidence interval')
plt.show()
def test_scikit_GP_SNRefsdal(self):
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
# add data
dm = -29.38 # D = 7.5e6 pc
# dm = -30.4 # D = 12.e6 pc
image = "S1"
bname = 'F160W'
curves = snrefsdal.read_curves(snrefsdal.path_data, image)
lc = curves.get(bname)
# lc.mshift = dm
t = lc.Time
y = lc.Mag
yerr = lc.Err
#
# Instanciate a Gaussian Process model
# kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
# Instanciate a Gaussian Process model
# kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
# gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)
gp = GaussianProcessRegressor()
# Fit to data using Maximum Likelihood Estimation of the parameters
X = np.atleast_2d(t).T
gp.fit(X, y)
# gp = GaussianProcessRegressor()
# Fit to data using Maximum Likelihood Estimation of the parameters
# gp.fit(t, y)
# Make the prediction on the meshed x-axis (ask for MSE as well)
# y_star, err_y_star = gp.predict(t, return_std=True)
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, sigma = gp.predict(t, return_std=True)
# k = gptools.SquaredExponentialKernel(param_bounds=[(min(np.abs(y)), max(np.abs(y))),
# (0, np.std(t))])
# # k = gptools.SquaredExponentialKernel(param_bounds=[(min(np.abs(y)), max(np.abs(y))),
# # (0, np.std(t))])
# gp = gptools.GaussianProcess(k)
# # gp = gptools.GaussianProcess(k, mu=gptools.LinearMeanFunction())
# gp.add_data(t, y, err_y=yerr)
#
# gp.optimize_hyperparameters()
# y_star, err_y_star = gp.predict(t)
fig = plt.figure()
ax = fig.add_axes((0.1, 0.3, 0.8, 0.65))
ax.invert_yaxis()
ax.plot(t, y, color='blue', label='L bol', lw=2.5)
ax.errorbar(t, y, yerr=yerr, fmt='o', color='blue', label='%s obs.')
#
# ax.plot(t, y_star, color='red', ls='--', lw=1.5, label='GP')
ax.plot(t, y_pred, '-', color='gray')
# ax.fill_between(t, y_star - 2 * err_y_star, y_star + 2 * err_y_star, color='gray', alpha=0.3)
ax.fill(np.concatenate([t, t[::-1]]),
np.concatenate([y_pred - 1.9600 * sigma,
(y_pred + 1.9600 * sigma)[::-1]]),
alpha=.5, fc='b', ec='None', label='95% confidence interval')
plt.show()
