#id, period = 11375941, 58.4
id, period = 18525697, 17.05
data = fetch_LINEAR_sample()
t, y, dy = data[id].T
#omega, power = search_frequencies(t, y, dy)
#period = omega[np.argmax(power)]
#print period
#exit()
omega = np.linspace(period, period + 0.1, 1000)
ax = plt.subplot(211)
for n_terms in [1, 2, 3]:
P1 = multiterm_periodogram(t, y, dy, omega, n_terms=n_terms)
plt.plot(omega, P1, lw=1, label='m = %i' % n_terms)
plt.legend(loc=2)
plt.xlim(period, period + 0.1)
plt.ylim(0, 1.0)
plt.ylabel('$1 - \chi^2(\omega) / \chi^2_{ref}$')
plt.subplot(212, sharex=ax)
for generalized in [True, False]:
if generalized:
label = 'generalized LS'
else:
label = 'standard LS'
P2 = lomb_scargle(t, y, dy, omega, generalized=generalized)
plt.plot(omega, P2, lw=1, label=label)
plt.legend(loc=2)
t, y, dy = data[14752041].T
omega0 = 17.217
# focus only on the region with the peak
omega1 = np.linspace(17.213, 17.220, 100)
omega2 = 0.5 * omega1
#------------------------------------------------------------
# Compute the delta BIC
terms = np.arange(1, 21)
BIC_max = np.zeros((2, len(terms)))
for i, omega in enumerate([omega1, omega2]):
for j in range(len(terms)):
P = multiterm_periodogram(t, y, dy, omega, terms[j])
BIC = lomb_scargle_BIC(P, y, dy, n_harmonics=terms[j])
BIC_max[i, j] = BIC.max()
#----------------------------------------------------------------------
# Plot the results
fig = plt.figure(figsize=(5, 3.75))
ax = [
fig.add_axes((0.15, 0.53, 0.8, 0.37)),
fig.add_axes((0.15, 0.1, 0.8, 0.37))
]
ax_inset = [
fig.add_axes((0.15 + 7 * 0.04, 0.55, 0.79 - 7 * 0.04, 0.17)),
fig.add_axes((0.15 + 7 * 0.04, 0.12, 0.79 - 7 * 0.04, 0.17))
]
t -= 0.4 * np.pi / omega0
width = 0.03
omega = np.linspace(omega0 - width, omega0 + width, 1000)
#------------------------------------------------------------
# Compute periodograms and best-fit solutions
# factor gives the factor that we're dividing the fundamental frequency by
factors = [1, 2]
nterms = [1, 6]
# Compute PSDs for factors & nterms
PSDs = dict()
for f in factors:
for n in nterms:
PSDs[(f, n)] = multiterm_periodogram(t, y, dy, omega / f, n)
# Compute the best-fit omega from the 6-term fit
omega_best = dict()
for f in factors:
omegaf = omega / f
PSDf = PSDs[(f, 6)]
omega_best[f] = omegaf[np.argmax(PSDf)]
# Compute the best-fit solution based on the fundamental frequency
best_fit = dict()
for f in factors:
for n in nterms:
mtf = MultiTermFit(omega_best[f], n)
mtf.fit(t, y, dy)
phase_best, y_best = mtf.predict(1000, adjust_offset=False)
import numpy as np
from astroML.datasets import fetch_LINEAR_sample
from astroML.time_series import multiterm_periodogram, MultiTermFit
from bombscargle.bombscargle import (MultiTermFit, MultiTermFitMCMC,
MultiTermMixtureFitFull,
MultiTermMixtureFit)
import matplotlib.pyplot as plt
data = fetch_LINEAR_sample()
t, y, dy = data[1004849].T
omega = np.linspace(13.4, 14, 10000)
PSD = multiterm_periodogram(t, y, dy, omega, 6)
#plt.plot(omega, PSD)
#plt.show()
#exit()
omega_best = omega[np.argmax(PSD)]
print omega_best
models = [MultiTermFit(omega_best, 6),
MultiTermFitMCMC(omega_best, 6),
MultiTermMixtureFitFull(omega_best, 6)
]
for model in models:
model = model.fit(t, y, dy)
print model.w_
phase_fit, y_fit, phased_t = model.predict(1000, return_phased_times=True)
t, y, dy = data[14752041].T
omega0 = 17.217
# focus only on the region with the peak
omega1 = np.linspace(17.213, 17.220, 100)
omega2 = 0.5 * omega1
#------------------------------------------------------------
# Compute the delta BIC
terms = np.arange(1, 21)
BIC_max = np.zeros((2, len(terms)))
for i, omega in enumerate([omega1, omega2]):
for j in range(len(terms)):
P = multiterm_periodogram(t, y, dy, omega, terms[j])
BIC = lomb_scargle_BIC(P, y, dy, n_harmonics=terms[j])
BIC_max[i, j] = BIC.max()
#----------------------------------------------------------------------
# Plot the results
fig = plt.figure()
ax = [fig.add_axes((0.15, 0.53, 0.8, 0.37)),
fig.add_axes((0.15, 0.1, 0.8, 0.37))]
ax_inset = [fig.add_axes((0.15 + 7 * 0.04, 0.55, 0.79 - 7 * 0.04, 0.17)),
fig.add_axes((0.15 + 7 * 0.04, 0.12, 0.79 - 7 * 0.04, 0.17))]
ylims = [(22750, 22850),
(26675, 26775)]
omega0 = [17.22, 8.61]
import numpy as np
from astroML.datasets import fetch_LINEAR_sample
from astroML.time_series import multiterm_periodogram, MultiTermFit
from bombscargle.bombscargle import (MultiTermFit, MultiTermFitMCMC,
MultiTermMixtureFitFull,
MultiTermMixtureFit)
import matplotlib.pyplot as plt
data = fetch_LINEAR_sample()
t, y, dy = data[1004849].T
omega = np.linspace(13.4, 14, 10000)
PSD = multiterm_periodogram(t, y, dy, omega, 6)
#plt.plot(omega, PSD)
#plt.show()
#exit()
omega_best = omega[np.argmax(PSD)]
print omega_best
models = [
MultiTermFit(omega_best, 6),
MultiTermFitMCMC(omega_best, 6),
MultiTermMixtureFitFull(omega_best, 6)
]
for model in models:
model = model.fit(t, y, dy)
print model.w_
phase_fit, y_fit, phased_t = model.predict(1000, return_phased_times=True)