def generate_lc(self,
pointing_index,
n_days,
gmag,
S82index,
random_state=None):
gen = RRLyraeGenerated(self.S82data.ids[S82index],
random_state=random_state)
pointing = self.pointings[pointing_index]
pointing = pointing[pointing['mjd'] <= pointing['mjd'].min() + n_days]
t = pointing['mjd']
filts = pointing['filter']
m5 = pointing['m5']
# generate magnitudes; no errors
mag = np.zeros_like(t)
for i, filt in enumerate('ugrizy'):
mask = (filts == i)
# HACK: for y-band, use z-band template
if filt == 'y': filt = 'z'
mag[mask] = gen.generated(filt, t[mask])
# adjust mags to desired r-band mean
gmag_mean = mag[filts == 1].mean()
mag += (gmag - gmag_mean)
# compute magnitude error from m5 (eq 5 of Ivezic 2014 LSST paper)
gamma = np.array([0.037, 0.038, 0.039, 0.039, 0.040, 0.040])
x = 10**(0.4 * (mag - m5))
sig2_rand = (0.04 - gamma[filts]) * x + gamma[filts] * x**2
sig2_sys = 0.005**2
dmag = np.sqrt(sig2_sys + sig2_rand)
rng = np.random.RandomState(random_state)
mag += dmag * rng.randn(len(mag))
return t, mag, dmag, filts
band separately, and taking a majority vote between the bands.
"""
import numpy as np
import matplotlib.pyplot as plt
# Use seaborn settings for plot styles
import seaborn
seaborn.set()
from gatspy.datasets import RRLyraeGenerated
from gatspy.periodic import (LombScargleAstroML, LombScargleMultiband,
NaiveMultiband)
# Choose a Sesar 2010 object to base our fits on
lcid = 1019544
rrlyrae = RRLyraeGenerated(lcid, random_state=0)
print("Extinction A_r = {0:.4f}".format(rrlyrae.obsmeta['rExt']))
# Generate data in a 6-month observing season
Nobs = 60
rng = np.random.RandomState(0)
nights = np.arange(180)
rng.shuffle(nights)
nights = nights[:Nobs]
# Find a subset of the simulated data. This is the same procedure as in
# fig_multiband_sim
t = 57000 + nights + 0.05 * rng.randn(Nobs)
dy = 0.06 + 0.01 * rng.randn(Nobs)
mags = np.array(
