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import numpy as np

def linear_data_sample(N=40, rseed=0, m=3, b=-2):

rng = np.random.RandomState(rseed)

x = 10 * rng.rand(N)

dy = m / 2 * (1 + rng.rand(N))

y = m * x + b + dy * rng.randn(N)

return (x, y, dy)

def linear_data_sample_big_errs(N=40, rseed=0, m=3, b=-2):

rng = np.random.RandomState(rseed)

x = 10 * rng.rand(N)

dy = m / 2 * (1 + rng.rand(N))

dy[20:25] *= 10

y = m * x + b + dy * rng.randn(N)

return (x, 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)

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)