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src/a/s/astroML-0.2/book_figures/chapter10/fig_LS_comparison.py   astroML(Download)
from matplotlib import pyplot as plt
 
from astroML.time_series import\
    lomb_scargle, search_frequencies, multiterm_periodogram
from astroML.datasets import fetch_LINEAR_sample
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)

src/a/s/astroML-HEAD/book_figures/chapter10/fig_LS_comparison.py   astroML(Download)
from matplotlib import pyplot as plt
 
from astroML.time_series import\
    lomb_scargle, search_frequencies, multiterm_periodogram
from astroML.datasets import fetch_LINEAR_sample
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)

src/a/s/astroML-0.2/book_figures/chapter10/fig_LS_double_eclipse.py   astroML(Download)
from matplotlib import pyplot as plt
 
from astroML.time_series import multiterm_periodogram, MultiTermFit
from astroML.datasets import fetch_LINEAR_sample
 
for f in factors:
    for n in nterms:
        PSDs[(f, n)] = multiterm_periodogram(t, y, dy, omega / f, n)
 
# Compute the best-fit omega from the 6-term fit

src/a/s/astroML-0.2/book_figures/chapter10/fig_LINEAR_BIC.py   astroML(Download)
from matplotlib import pyplot as plt
 
from astroML.time_series import multiterm_periodogram, lomb_scargle_BIC
from astroML.datasets import fetch_LINEAR_sample
 
for i, omega in enumerate([omega1, omega2]):
    for j in range(len(terms)):
        P = multiterm_periodogram(t, y, dy, omega, terms[j])
        BIC = lomb_scargle_BIC(P, y, dy, n_harmonics=terms[j])
        BIC_max[i, j] = BIC.max()

src/a/s/astroML-HEAD/book_figures/chapter10/fig_LS_double_eclipse.py   astroML(Download)
from matplotlib import pyplot as plt
 
from astroML.time_series import multiterm_periodogram, MultiTermFit
from astroML.datasets import fetch_LINEAR_sample
 
for f in factors:
    for n in nterms:
        PSDs[(f, n)] = multiterm_periodogram(t, y, dy, omega / f, n)
 
# Compute the best-fit omega from the 6-term fit

src/a/s/astroML-HEAD/book_figures/chapter10/fig_LINEAR_BIC.py   astroML(Download)
from matplotlib import pyplot as plt
 
from astroML.time_series import multiterm_periodogram, lomb_scargle_BIC
from astroML.datasets import fetch_LINEAR_sample
 
for i, omega in enumerate([omega1, omega2]):
    for j in range(len(terms)):
        P = multiterm_periodogram(t, y, dy, omega, terms[j])
        BIC = lomb_scargle_BIC(P, y, dy, n_harmonics=terms[j])
        BIC_max[i, j] = BIC.max()

src/a/s/astroML-0.2/book_figures/chapter10/compute_periods.py   astroML(Download)
import numpy as np
from astroML.datasets import fetch_LINEAR_sample
from astroML.time_series import lomb_scargle, multiterm_periodogram, \
    search_frequencies
 

src/a/s/astroML-HEAD/book_figures/chapter10/compute_periods.py   astroML(Download)
import numpy as np
from astroML.datasets import fetch_LINEAR_sample
from astroML.time_series import lomb_scargle, multiterm_periodogram, \
    search_frequencies