def gatspy_period(lc_g):
time, mag, error = np.array(lc_g['MJD'].values), np.array(lc_g['MAG_KRON'].values), np.array(lc_g['MAGERR_KRON'].values)
per_f = (np.max(time) - np.min(time))*10
periods = np.linspace(.01, per_f, 10000)
model = LombScargle(fit_offset=True).fit(time, mag, error)
power = model.score(periods)
best_per = periods[np.argmax(power)]
return best_per
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))
fig.savefig('naive_{0}.png'.format(i + 1))
plt.show()
for ii in range(N_trials):
for jj in range(len(W)):
# maximum frequency/min period
fmax = W[jj]/t.max()
# "data" array
y = np.random.normal(0, 1.0, t.size)
ls = LombScargle().fit(t, y, 1.0)
ls.optimizer.quiet = True
ls.optimizer.period_range = (1/fmax, t[-1])
chisq_0 = np.var(y)
scores[ii, jj] = ls.score(ls.best_period) * chisq_0 * 0.5 * (N_pts-1)
# fout = open("output{0}.dat".format(fnum), "w")
# for ii in range(N_trials):
# fout.write("{0},{1}\n".format(scores[ii,0], scores[ii,1]))
# fout.close()
# data = data[:100000]
# data = scores * (N_pts-1)
# qw1 = np.percentile(data.T[0], [75,25])
# qw2 = np.percentile(data.T[1], [75,25])
# qw1 = qw1[0] - qw1[1]
# qw2 = qw2[0] - qw2[1]
