/
test_synthetic_data.py
200 lines (160 loc) · 8.39 KB
/
test_synthetic_data.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
"""Use synthetic data to verify lightkurve detrending and signal recovery.
"""
from __future__ import division, print_function
from astropy.utils.data import get_pkg_data_filename
from astropy.stats.bls import BoxLeastSquares
import numpy as np
import pytest
from scipy import stats
from ..targetpixelfile import KeplerTargetPixelFile
from ..correctors import SFFCorrector, PLDCorrector
# See `data/synthetic/README.md` for details about these synthetic test files
filename_synthetic_sine = get_pkg_data_filename("data/synthetic/synthetic-k2-sinusoid.targ.fits.gz")
filename_synthetic_transit = get_pkg_data_filename("data/synthetic/synthetic-k2-planet.targ.fits.gz")
filename_synthetic_flat = get_pkg_data_filename("data/synthetic/synthetic-k2-flat.targ.fits.gz")
def test_sine_sff():
"""Can we recover a synthetic sine curve using SFF and LombScargle?"""
# Retrieve the custom, known signal properties
tpf = KeplerTargetPixelFile(filename_synthetic_sine)
true_period = np.float(tpf.hdu[3].header['PERIOD'])
true_amplitude = np.float(tpf.hdu[3].header['SINE_AMP'])
# Run the SFF algorithm
lc = tpf.to_lightcurve()
corrector = SFFCorrector(lc)
cor_lc = corrector.correct(tpf.pos_corr2, tpf.pos_corr1,
niters=4, windows=1, bins=7, restore_trend=True, timescale=0.5)
# Verify that we get the period within ~20%
pg = cor_lc.to_periodogram(method='lombscargle', minimum_period=1,
maximum_period=10, oversample_factor=10)
ret_period = pg.period_at_max_power.value
threshold = 0.2
assert ((ret_period > true_period*(1-threshold)) &
(ret_period < true_period*(1+threshold)) )
# Verify that we get the amplitude to within 10%
n_cad = len(tpf.time)
design_matrix = np.vstack([np.ones(n_cad),
np.sin(2.0*np.pi*cor_lc.time.value/ret_period),
np.cos(2.0*np.pi*cor_lc.time.value/ret_period)]).T
ATA = np.dot(design_matrix.T, design_matrix / cor_lc.flux_err[:, None]**2)
least_squares_coeffs = np.linalg.solve(ATA, np.dot(design_matrix.T, cor_lc.flux/cor_lc.flux_err**2 ))
const, sin_weight, cos_weight = least_squares_coeffs
fractional_amplitude = (sin_weight**2+cos_weight**2)**(0.5) / const
assert ((fractional_amplitude > true_amplitude/1.1) &
(fractional_amplitude < true_amplitude*1.1) )
def test_transit_sff():
"""Can we recover a synthetic exoplanet signal using SFF and BLS?"""
# Retrieve the custom, known signal properties
tpf = KeplerTargetPixelFile(filename_synthetic_transit)
true_period = np.float(tpf.hdu[3].header['PERIOD'])
true_rprs = np.float(tpf.hdu[3].header['RPRS'])
true_transit_lc = tpf.hdu[3].data['NOISELESS_INPUT']
max_depth = 1-np.min(true_transit_lc)
# Run the SFF algorithm
lc = tpf.to_lightcurve().normalize()
corrector = SFFCorrector(lc)
cor_lc = corrector.correct(tpf.pos_corr2, tpf.pos_corr1,
niters=4, windows=1, bins=7, restore_trend=False, timescale=0.5)
# Verify that we get the transit period within 5%
pg = cor_lc.to_periodogram(method='bls', minimum_period=1, maximum_period=9,
frequency_factor=0.05, duration=np.arange(0.1, 0.6, 0.1))
ret_period = pg.period_at_max_power.value
threshold = 0.05
assert ((ret_period > true_period*(1-threshold)) &
(ret_period < true_period*(1+threshold)))
# Verify that we get the transit depth in expected bounds
assert ((pg.depth_at_max_power >= true_rprs**2) &
(pg.depth_at_max_power < max_depth))
def test_transit_pld():
"""Can we recover a synthetic exoplanet signal using PLD and BLS?"""
# Retrieve the custom, known signal properties
tpf = KeplerTargetPixelFile(filename_synthetic_transit)
true_period = np.float(tpf.hdu[3].header['PERIOD'])
true_rprs = np.float(tpf.hdu[3].header['RPRS'])
true_transit_lc = tpf.hdu[3].data['NOISELESS_INPUT']
max_depth = 1-np.min(true_transit_lc)
# Run the PLD algorithm on a first pass
corrector = PLDCorrector(tpf)
cor_lc = corrector.correct()
pg = cor_lc.to_periodogram(method='bls', minimum_period=1, maximum_period=9,
frequency_factor=0.05, duration=np.arange(0.1, 0.6, 0.1))
# Re-do PLD with the suspected transits masked
cor_lc = corrector.correct(cadence_mask=~pg.get_transit_mask()).normalize()
pg = cor_lc.to_periodogram(method='bls', minimum_period=1, maximum_period=9,
frequency_factor=0.05, duration=np.arange(0.1, 0.6, 0.1))
# Verify that we get the period within ~5%
ret_period = pg.period_at_max_power.value
threshold = 0.05
assert ((ret_period > true_period*(1-threshold)) &
(ret_period < true_period*(1+threshold)))
# Verify that we get the transit depth in expected bounds
assert ((pg.depth_at_max_power >= true_rprs**2) &
(pg.depth_at_max_power < max_depth))
def test_sine_pld():
"""Can we recover a synthetic sine wave using PLD and LombScargle?"""
# Retrieve the custom, known signal properties
tpf = KeplerTargetPixelFile(filename_synthetic_sine)
true_period = np.float(tpf.hdu[3].header['PERIOD'])
true_amplitude = np.float(tpf.hdu[3].header['SINE_AMP'])
# Run the PLD algorithm
corrector = tpf.to_corrector('pld')
cor_lc = corrector.correct()
# Verify that we get the period within ~20%
pg = cor_lc.to_periodogram(method='lombscargle', minimum_period=1,
maximum_period=10, oversample_factor=10)
ret_period = pg.period_at_max_power.value
threshold = 0.2
assert ((ret_period > true_period*(1-threshold)) &
(ret_period < true_period*(1+threshold)) )
# Verify that we get the amplitude to within 20%
n_cad = len(tpf.time)
design_matrix = np.vstack([np.ones(n_cad),
np.sin(2.0*np.pi*cor_lc.time.value/ret_period),
np.cos(2.0*np.pi*cor_lc.time.value/ret_period)]).T
ATA = np.dot(design_matrix.T, design_matrix / cor_lc.flux_err[:, None]**2)
least_squares_coeffs = np.linalg.solve(ATA, np.dot(design_matrix.T, cor_lc.flux/cor_lc.flux_err**2 ))
const, sin_weight, cos_weight = least_squares_coeffs
fractional_amplitude = (sin_weight**2+cos_weight**2)**(0.5) / const
assert ((fractional_amplitude > true_amplitude/1.1) &
(fractional_amplitude < true_amplitude*1.1) )
def test_detrending_residuals():
"""Test the detrending residual distributions"""
# Retrieve the custom, known signal properties
tpf = KeplerTargetPixelFile(filename_synthetic_flat)
# Run the SFF algorithm
lc = tpf.to_lightcurve()
corrector = SFFCorrector(lc)
cor_lc = corrector.correct(tpf.pos_corr2, tpf.pos_corr1,
niters=10, windows=5, bins=7, restore_trend=True)
# Verify that we get a significant reduction in RMS
cdpp_improvement = lc.estimate_cdpp() / cor_lc.estimate_cdpp()
assert cdpp_improvement > 10.0
# The residuals should be Gaussian-"ish"
# Table 4.1 of Ivezic, Connolly, Vanerplas, Gray 2014
anderson_threshold = 1.57
resid_n_sigmas = (cor_lc.flux - np.mean(cor_lc.flux))/cor_lc.flux_err
A_value, _, _ = stats.anderson(resid_n_sigmas)
assert A_value**2 < anderson_threshold
n_sigma = np.std(resid_n_sigmas)
assert n_sigma < 2.0
corrector = tpf.to_corrector('pld')
cor_lc = corrector.correct(restore_trend=False)
cdpp_improvement = lc.estimate_cdpp()/cor_lc.estimate_cdpp()
assert cdpp_improvement > 10.0
resid_n_sigmas = (cor_lc.flux - np.mean(cor_lc.flux))/cor_lc.flux_err
A_value, crit, sig = stats.anderson(resid_n_sigmas)
assert A_value**2 < anderson_threshold
n_sigma = np.std(resid_n_sigmas)
assert n_sigma < 2.0
def test_centroids():
"""Test the estimate centroid method."""
for fn in (filename_synthetic_sine, filename_synthetic_transit,
filename_synthetic_flat):
tpf = KeplerTargetPixelFile(fn)
xraw, yraw = tpf.estimate_centroids()
xnorm = xraw - np.median(xraw)
ynorm = yraw - np.median(yraw)
xposc = tpf.pos_corr2 - np.median(tpf.pos_corr2)
yposc = tpf.pos_corr1 - np.median(tpf.pos_corr1)
rmax = np.max(np.sqrt((xnorm.value-xposc)**2 + (ynorm.value-yposc)**2))
# The centroids should agree to within a hundredth of a pixel.
assert rmax < 0.01