def get_multiharmonic_periodogram(x, y, err, nh, hfac=3, ofac=10):
model = LombScargle(Nterms=nh)
model.fit(x, y, err)
pers, p = model.periodogram_auto(nyquist_factor=hfac, oversampling=ofac)
return np.power(pers, -1), p
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))
