In [1]:

    

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn; seaborn.set()


    

In [2]:

    

from multiband_LS.data import fetch_light_curves


    

In [3]:

    

rrlyrae = fetch_light_curves(partial=True)


    

In [4]:

    

ids = list(rrlyrae.ids)
lcid = ids[1]


    

In [5]:

    

t, y, dy, filts = rrlyrae.get_lightcurve(lcid)


    

In [6]:

    

for filt in 'ugriz':
    mask = (filts == filt)
    plt.errorbar(t[mask] % rrlyrae.get_metadata(lcid)['P'], y[mask], dy[mask], fmt='o')


    

In [7]:

    

from multiband_LS import LombScargleMultiband, SuperSmoother


    

In [10]:

    

class SuperSmoother1Band(SuperSmoother):
    """Convenience class to fit a single band of data with supersmoother"""
    def __init__(self, optimizer=None, band='g'):
        self.band = band
        SuperSmoother.__init__(self, optimizer)

    def _fit(self, t, y, dy, filts):
        mask = (filts == self.band)
        self.t, self.y, self.dy = t[mask], y[mask], dy[mask]
        self.filts = None
        return SuperSmoother._fit(self, self.t, self.y, self.dy, self.filts)


    

In [17]:

    

def do_plot(i=0):
    lcid = ids[i]
    t, y, dy, filts = rrlyrae.get_lightcurve(lcid)

    periods = np.linspace(0.2, 1.2, 10000)

    fig = plt.figure(figsize=(12, 4))
    gs = plt.GridSpec(2, 2)
    ax = [fig.add_subplot(gs[:, 0]),
          fig.add_subplot(gs[0, 1]),
          fig.add_subplot(gs[1, 1])]
    for filt in 'ugriz':
        mask = (filts == filt)
        ax[0].errorbar(t[mask] % rrlyrae.get_metadata(lcid)['P'], y[mask], dy[mask], fmt='o')
    
    #model = SuperSmoother()
    #mask = (filts == 'g')
    #model.fit(t[mask], y[mask], dy[mask])
    model = SuperSmoother1Band(band='g')
    model.fit(t, y, dy, filts)
    ax[1].plot(periods, model.score(periods), lw=1)
    ax[1].set_ylim(0, 1)
    print("total observations: {0}".format(len(t)))
    print("g-band observations: {0}".format(mask.sum()))

    model = LombScargleMultiband(Nterms_base=1, Nterms_band=0)
    model.fit(t, y, dy, filts)
    ax[2].plot(periods, model.score(periods), lw=1)
    ax[2].set_ylim(0, 1)    

    #P_best, scores = model.find_best_periods(5, True)
    #ax[2].plot(P_best, scores, '.k')

    print(rrlyrae.get_metadata(lcid)['P'])


    

In [18]:

    

do_plot(2)


    

    




total observations: 57
g-band observations: 9
0.446055






    

In [16]:

    

do_plot(2)


    

    




total observations: 57
g-band observations: 15
0.446055






    

In [ ]: