%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats

# use seaborn plotting defaults
# If this causes an error, you can comment it out.
import seaborn as sns
sns.set()

from astroML.datasets import fetch_LINEAR_geneva

data = fetch_LINEAR_geneva()

print(data.shape)

data[0]

print(data.dtype.names)

data['RA']

feature_names = ['ug', 'gi', 'iK', 'JK', 'logP', 'amp', 'skew']
X = np.vstack([data[f] for f in feature_names]).T
X.shape

data['LINEARobjectID']

from astroML.datasets import fetch_LINEAR_sample
lightcurves = fetch_LINEAR_sample()

print(lightcurves)

lc = lightcurves.get_light_curve(10003298)
lc.shape

t, y, dy = lc.T

plt.errorbar(t, y, dy, fmt='o');

lc_index = 20
lc_id = data['LINEARobjectID'][lc_index]
lc_id

lc = lightcurves.get_light_curve(int(lc_id))
t, y, dy = lc.T

P = 10 ** data['logP'][lc_index]
phase = t % P
plt.errorbar(phase, y, dy, fmt='o');