def sample_light_curve_2(phased=True):
from astroML.datasets import fetch_LINEAR_sample
data = fetch_LINEAR_sample()
t, y, dy = data[10022663].T
if phased:
P_best = 0.61596079804
t /= P_best
return (t, y, dy)
def sample_light_curve(phased=True):
from astroML.datasets import fetch_LINEAR_sample
data = fetch_LINEAR_sample()
t, y, dy = data[18525697].T
if phased:
P_best = 0.580313015651
t /= P_best
return (t, y, dy)
def sample_light_curve_2(phased=True):
from astroML.datasets import fetch_LINEAR_sample
data = fetch_LINEAR_sample()
t, y, dy = data[10022663].T
if phased:
P_best = 0.61596079804
t /= P_best
return (t, y, dy)
def sample_light_curve(phased=True):
from astroML.datasets import fetch_LINEAR_sample
data = fetch_LINEAR_sample()
t, y, dy = data[18525697].T
if phased:
P_best = 0.580313015651
t /= P_best
return (t, y, dy)
# Author: Jake VanderPlas <*****@*****.**>
# License: BSD
# The figure produced by this code is published in the textbook
# "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
# For more information, see http://astroML.github.com
import numpy as np
from matplotlib import pyplot as plt
from astroML.time_series import\
lomb_scargle, search_frequencies, multiterm_periodogram
from astroML.datasets import fetch_LINEAR_sample
#id, period = 11375941, 58.4
id, period = 18525697, 17.05
data = fetch_LINEAR_sample()
t, y, dy = data[id].T
#omega, power = search_frequencies(t, y, dy)
#period = omega[np.argmax(power)]
#print period
#exit()
omega = np.linspace(period, period + 0.1, 1000)
ax = plt.subplot(211)
for n_terms in [1, 2, 3]:
P1 = multiterm_periodogram(t, y, dy, omega, n_terms=n_terms)
plt.plot(omega, P1, lw=1, label='m = %i' % n_terms)
plt.legend(loc=2)
plt.xlim(period, period + 0.1)
plt.ylim(0, 1.0)
def get_linear_id_list():
data = fetch_LINEAR_sample()
return data.ids
def __init__(self, id = 10040133):
self.LINEAR_data = fetch_LINEAR_sample()
self.id = id
self.t, self.mag, self.dmag = self.LINEAR_data.get_light_curve(self.id).T
