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))
]
ylims = [(22750, 22850), (26675, 26775)]
ax.set_title('Corrected version')
# Second panel: periodogram
ax = fig.add_subplot(212)
ax.plot(omega, P_S, '--k', lw=1, label='standard')
ax.plot(omega, P_G, '-k', lw=1, label='generalized')
ax.legend(loc=2)
# plot the significance lines.
xlim = (omega[0], omega[-1])
ax.plot(xlim, [sig1, sig1], ':', c='black')
ax.plot(xlim, [sig5, sig5], ':', c='black')
# label BIC on the right side
ax2 = ax.twinx()
ax2.set_ylim(tuple(lomb_scargle_BIC(ax.get_ylim(), y_obs, dy)))
ax2.set_ylabel(r'$\Delta BIC$')
ax.set_xlabel('$\omega$')
ax.set_ylabel(r'$P_{\rm LS}(\omega)$')
ax.set_ylim(0, 1.1)
#######################################################################
# Redo the plot without the typo
# We need a larger data range to actually get significant power
# with actual noisy data
#------------------------------------------------------------
# Generate data where y is positive
np.random.seed(0)
N = 300
ax.set_xlim(mag1date[0] - 50, mag1date[-1] + 50)
ax.set_ylim(16, max(mag1) + 0.5)
# Second panel: the periodogram & significance levels
ax1 = fig.add_subplot(212, xscale='log')
ax1.plot(period, PS, '-', c='black', lw=1, zorder=1)
ax1.plot([period[0], period[-1]], [sig1, sig1], ':', c='black')
ax1.plot([period[0], period[-1]], [sig5, sig5], ':', c='black')
#ax1.annotate("", (0.3, 0.65), (0.3, 0.85), ha='center',
# arrowprops=dict(arrowstyle='->'))
ax1.set_xlim(period[0], period[-1])
ax1.set_ylim(-0.05, 0.85)
ax1.set_xlabel(r'period (days)')
ax1.set_ylabel('power')
# Twin axis: label BIC on the right side
ax2 = ax1.twinx()
ax2.set_ylim(tuple(lomb_scargle_BIC(ax1.get_ylim(), mag1, mag1sig)))
ax2.set_ylabel(r'$\Delta BIC$')
ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))
ax1.xaxis.set_minor_formatter(plt.FormatStrFormatter('%.1f'))
ax1.xaxis.set_major_locator(plt.LogLocator(10))
ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%.3g'))
plt.savefig("042817_Lomb_scargle_R_band.pdf")
ax.set_xlabel('time (days)')
ax.set_ylabel('flux')
ax.set_xlim(-5, 105)
# Second panel: the periodogram & significance levels
ax1 = fig.add_subplot(212, xscale='log')
ax1.plot(period, PS, '-', c='black', lw=1, zorder=1)
ax1.plot([period[0], period[-1]], [sig1, sig1], ':', c='black')
ax1.plot([period[0], period[-1]], [sig5, sig5], ':', c='black')
ax1.annotate(" ", (0.3, 0.65), (0.3, 0.85), ha='center',
arrowprops=dict(arrowstyle='->'))
ax1.set_xlim(period[0], period[-1])
ax1.set_ylim(-0.05, 0.85)
ax1.set_xlabel(r'period (days)')
ax1.set_ylabel('power')
# Twin axis: label BIC on the right side
ax2 = ax1.twinx()
ax2.set_ylim(tuple(lomb_scargle_BIC(ax1.get_ylim(), y_obs, dy)))
ax2.set_ylabel(r'$\Delta BIC$')
ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))
ax1.xaxis.set_minor_formatter(plt.FormatStrFormatter('%.1f'))
ax1.xaxis.set_major_locator(plt.LogLocator(10))
ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%.3g'))
plt.show()
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]
for i in range(2):
