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