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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:
        mtf = MultiTermFit(omega_best[f], n)
        mtf.fit(t, y, dy)
        phase_best, y_best = mtf.predict(1000, adjust_offset=False)

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:
        mtf = MultiTermFit(omega_best[f], n)
        mtf.fit(t, y, dy)
        phase_best, y_best = mtf.predict(1000, adjust_offset=False)

src/a/s/astroML-0.2/book_figures/chapter10/fig_LINEAR_LS.py   astroML(Download)
 
from astroML.decorators import pickle_results
from astroML.time_series import search_frequencies, lomb_scargle, MultiTermFit
from astroML.datasets import fetch_LINEAR_sample
 
 
    # do a fit to the first 4 Fourier components
    mtf = MultiTermFit(omega_best, 4)
    mtf.fit(t, y, dy)
    phase_fit, y_fit, phased_t = mtf.predict(1000, return_phased_times=True)

src/a/s/astroML-HEAD/book_figures/chapter10/fig_LINEAR_LS.py   astroML(Download)
 
from astroML.decorators import pickle_results
from astroML.time_series import search_frequencies, lomb_scargle, MultiTermFit
from astroML.datasets import fetch_LINEAR_sample
 
 
    # do a fit to the first 4 Fourier components
    mtf = MultiTermFit(omega_best, 4)
    mtf.fit(t, y, dy)
    phase_fit, y_fit, phased_t = mtf.predict(1000, return_phased_times=True)