ax[0].errorbar(phase[mask], mag[mask], dmag[mask], fmt='o', color='#666666')
ax[0].errorbar(phase[~mask], mag[~mask], dmag[~mask], fmt='o', color='#CCCCCC')
ax[0].invert_yaxis()
for fit_offset in [False, True]:
i = int(fit_offset)
model = LombScargle(fit_offset=fit_offset).fit(t[mask], mag[mask],
dmag[mask])
P = model.periodogram(periods)
if fit_offset:
label = 'floating mean'
else:
label = 'standard'
lines = ax[0].plot(phasefit,
model.predict(tfit, period=rrlyrae.period),
label=label)
ax[1 + i].plot(periods, P, lw=1, c=lines[0].get_color())
ax[1 + i].set_title('{0} Periodogram'.format(label.title()))
ax[1 + i].set_ylabel('power')
ax[1 + i].set_ylim(0, 1)
ax[1 + i].set_xlim(0.2, 1.4)
ax[0].legend(loc='upper left')
ax[0].set_xlabel('phase')
ax[0].set_ylabel('magnitude')
ax[0].set_title('Phased data (period=0.622 days)')
ax[1].xaxis.set_major_formatter(plt.NullFormatter())
ax[2].set_xlabel('Period (days)')
fig.add_subplot(gs[0, 1])]
# Plot the data
ax[0].errorbar(t, mag, dmag, fmt='o', color='#333333')
ax[1].errorbar(phase, mag, dmag, fmt='.', color='#888888')
# Fit and plot the model
model = LombScargle().fit(t, mag, dmag)
model.optimizer.period_range = (0.2, 1.2)
phase = (t / model.best_period) % 1
phasefit = np.linspace(0, 1, 1000)
tfit = model.best_period * phasefit
lines = ax[2].plot(periods, model.periodogram(periods), lw=1)
ax[1].plot(phasefit, model.predict(tfit),
c=lines[0].get_color())
ax[1].invert_yaxis()
ax[0].set_xlabel('date of observation (MJD)')
ax[0].set_ylabel('magnitude')
ax[0].set_title('Input Data')
ax[0].invert_yaxis()
ax[1].set_title('Folded Data (P={0:.3f} days)'.format(rrlyrae.period))
ax[1].set_xlabel('phase')
ax[1].yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax[2].set_title('Periodogram')
ax[2].set_xlabel('period (days)')
ax[2].set_ylabel('power')
for i in range(2):
fig, ax = plt.subplots(2, 2, figsize=(10, 4), sharex='col')
fig.subplots_adjust(left=0.07, right=0.95, wspace=0.1, bottom=0.15)
if i == 0:
ind = np.arange(Nobs) % 5
y = mags[ind, np.arange(Nobs)]
f = np.array(filts)[ind]
else:
arrs = np.broadcast_arrays(t, mags, dy, filts[:, None])
t, y, dy, f = map(np.ravel, arrs)
model1 = LombScargle()
model1.fit(t, y, dy)
yfit = model1.predict(tfit, period=period)
plot_data(ax[0, 0], t, y, dy, f)
ax[0, 0].plot(tfit / period, yfit, '-', color='gray', lw=4, alpha=0.5)
ax[0, 1].plot(periods, model1.score(periods), color='gray')
ax[0, 0].set_xlabel('')
model2 = LombScargleMultiband(Nterms_base=1, Nterms_band=1)
model2.fit(t, y, dy, f)
yfits = model2.predict(tfit, filts=filts[:, None], period=period)
plot_data(ax[1, 0], t, y, dy, f)
for j in range(5):
ax[1, 0].plot(tfit / period, yfits[j])
ax[1, 1].plot(periods, model2.score(periods))
plot_data(ax)
ax.set_title('Input Data')
plt.savefig('buildup_1.png')
# Plot the base model
fig, ax = plt.subplots()
plot_data(ax)
t_all = np.ravel(t * np.ones_like(mags))
mags_all = np.ravel(mags)
dy_all = np.ravel(dy * np.ones_like(mags))
basemodel = LombScargle(Nterms=2).fit(t_all, mags_all, dy_all)
period = rrlyrae.period
tfit = np.linspace(0, period, 1000)
base_fit = basemodel.predict(tfit, period=period)
ax.plot(tfit / period, base_fit, color='black', lw=5, alpha=0.5)
ax.set_title('2-term Base Model')
# Plot the band-by-band augmentation
multimodel = LombScargleMultiband(Nterms_base=2, Nterms_band=1)
t1, y1, dy1, f1 = map(np.ravel,
np.broadcast_arrays(t, mags, dy, filts[:, None]))
multimodel.fit(t1, y1, dy1, f1)
yfits = multimodel.predict(tfit, filts=filts[:, None], period=period)
plt.savefig('buildup_2.png')
fig, ax = plt.subplots()
color='#666666')
ax[0].errorbar(phase[~mask], mag[~mask], dmag[~mask], fmt='o',
color='#CCCCCC')
ax[0].invert_yaxis()
for fit_offset in [False, True]:
i = int(fit_offset)
model = LombScargle(fit_offset=fit_offset).fit(t[mask],
mag[mask], dmag[mask])
P = model.periodogram(periods)
if fit_offset:
label = 'floating mean'
else:
label = 'standard'
lines = ax[0].plot(phasefit,
model.predict(tfit, period=rrlyrae.period),
label=label)
ax[1 + i].plot(periods, P, lw=1, c=lines[0].get_color())
ax[1 + i].set_title('{0} Periodogram'.format(label.title()))
ax[1 + i].set_ylabel('power')
ax[1 + i].set_ylim(0, 1)
ax[1 + i].set_xlim(0.2, 1.4)
ax[0].legend(loc='upper left')
ax[0].set_xlabel('phase')
ax[0].set_ylabel('magnitude')
ax[0].set_title('Phased data (period=0.622 days)')
ax[1].xaxis.set_major_formatter(plt.NullFormatter())
ax[2].set_xlabel('Period (days)')
# Plot the data
ax[0].errorbar(phase, mag, dmag, fmt='o', color='#AAAAAA')
# Plot the fits
for i, Nterms in enumerate(models):
model = LombScargle(Nterms=Nterms).fit(t, mag, dmag)
P = model.periodogram(periods)
label = "{0} terms".format(Nterms)
if Nterms == 1:
label = label[:-1]
alpha = 1.0 if (Nterms < 20) else 0.5
lines = ax[0].plot(phasefit, model.predict(tfit, period=rrlyrae.period),
label=label, alpha=alpha)
ax[1 + i].plot(periods, model.periodogram(periods),
color=lines[0].get_color(), lw=1)
ax[1 + i].set_title("{0}-term Periodogram".format(Nterms))
ax[1 + i].set_xlim(0.2, 1.4)
ax[1 + i].set_ylim(0, 1)
ax[2].set_ylabel('power')
ax[3].set_xlabel('period (days)')
for i in [1, 2]:
ax[i].xaxis.set_major_formatter(plt.NullFormatter())
ax[0].set_xlabel('phase')
ax[0].set_ylabel('magnitude')
]
# Plot the data
ax[0].errorbar(t, mag, dmag, fmt='o', color='#333333')
ax[1].errorbar(phase, mag, dmag, fmt='.', color='#888888')
# Fit and plot the model
model = LombScargle().fit(t, mag, dmag)
model.optimizer.period_range = (0.2, 1.2)
phase = (t / model.best_period) % 1
phasefit = np.linspace(0, 1, 1000)
tfit = model.best_period * phasefit
lines = ax[2].plot(periods, model.periodogram(periods), lw=1)
ax[1].plot(phasefit, model.predict(tfit), c=lines[0].get_color())
ax[1].invert_yaxis()
ax[0].set_xlabel('date of observation (MJD)')
ax[0].set_ylabel('magnitude')
ax[0].set_title('Input Data')
ax[0].invert_yaxis()
ax[1].set_title('Folded Data (P={0:.3f} days)'.format(rrlyrae.period))
ax[1].set_xlabel('phase')
ax[1].yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax[2].set_title('Periodogram')
ax[2].set_xlabel('period (days)')
ax[2].set_ylabel('power')
ax[2].set_xlim(0.2, 1.4)
