def measure_period(ticid, t, corrected_y, yerr):
print("Measure the rotation period...")
rotate = ss.RotationModel(t, corrected_y, yerr)
ls_period = rotate.ls_rotation()
acf_period = rotate.acf_rotation(interval=0.00138889,
cutoff=1.5,
window_length=1999)
period_grid = np.arange(.5, 3 * ls_period, 30. / 1000.)
if ls_period > 10:
period_grid = np.linspace(.5, 30, 1000.)
pdm_period = rotate.pdm_rotation(period_grid)
# Make the figure
fig = rotate.big_plot(methods=["pdm", "acf", "ls"],
xlim=(0, 100),
method_xlim=(-1, 50))
rvar = np.log10(rotate.Rvar * 1e6)
print("log10(Rvar) = ", rvar, "ppm")
plt.title("log10(Rvar) = {0:.2f}".format(rvar), fontsize=20)
fig.savefig("plots/{}_plot".format(ticid))
# Get peak heights
ls_peak_pos, ls_peak_height = rt.get_peak_statistics(
1. / rotate.freq, rotate.power)
acf_peak_pos, acf_peak_height = rt.get_peak_statistics(
rotate.lags, rotate.acf)
pdm_peak_pos, pdm_peak_height = rt.get_peak_statistics(
rotate.phase, -rotate.phis)
return rvar, ls_period, acf_period, pdm_period[0], pdm_period[1], \
ls_peak_height[:3], ls_peak_pos[:3], acf_peak_height[:3], \
acf_peak_pos[:3], pdm_peak_height[:3], pdm_peak_pos[:3]
def make_rotation_plots(time, flux, flux_err, interval=0.02043365,
period_grid=np.linspace(1, 50, 2000)):
rotate = ss.RotationModel(time, flux, flux_err)
ls_period = rotate.ls_rotation()
acf_period = rotate.acf_rotation(interval)
# pdm_period, period_err = rotate.pdm_rotation(rotate.lags, pdm_nbins=10)
pdm_period, period_err = rotate.pdm_rotation(period_grid, pdm_nbins=10)
fig = rotate.big_plot()
return ls_period, acf_period, pdm_period, period_err, fig
def test_rvar():
time = np.linspace(0, 100, 1000)
p = 10
w = 2*np.pi/p
flux = np.sin(w*time)
flux_err = np.ones_like(flux)*1e-2
star = ss.RotationModel(time, flux, flux_err)
Rvar = star.calc_Rvar()
total_range = max(flux) - min(flux)
assert np.isclose(Rvar, total_range, atol=.1)
def test_big_plot():
# Generate some data
time = np.linspace(0, 100, 1000)
p = 10
w = 2 * np.pi / p
flux = np.sin(w * time) + np.random.randn(len(time)) * 1e-2
flux_err = np.ones_like(flux) * 1e-2
rotate = ss.RotationModel(time, flux, flux_err)
ls_period = rotate.ls_rotation()
acf_period = rotate.acf_rotation(interval=0.02043365)
pdm_period, period_err = rotate.pdm_rotation(rotate.lags, pdm_nbins=10)
def test_run():
starname = "TIC 10863087"
lcf = lk.search_lightcurvefile(starname).download()
lc = lcf.PDCSAP_FLUX
no_nan_lc = lc.remove_nans()
clipped_lc = no_nan_lc.remove_outliers(sigma=3)
clipped_lc.scatter(alpha=.5, s=.5)
rotate = ss.RotationModel(clipped_lc.time, clipped_lc.flux,
clipped_lc.flux_err)
rotate.lc_plot()
ls_period = rotate.ls_rotation()
rotate.ls_plot()
tess_cadence = 1. / 24. / 30. # This is a TESS 2 minute cadence star.
acf_period = rotate.acf_rotation(tess_cadence)
rotate.acf_plot()
period_grid = np.linspace(.1, 2, 1000)
pdm_period, period_err = rotate.pdm_rotation(period_grid, pdm_nbins=10)
print(pdm_period, period_err)
rotate.pdm_plot()
# Lomb-Scargle periodogram
period_array = 1. / rotate.freq
power_array = rotate.power
# Autocorrelation function
ACF_array = rotate.acf
lag_array = rotate.lags
# Phase-dispersion minimization
phi_array = rotate.phis # The 'dispersion' plotted in the lower panel above.
period_grid = period_grid # We already defined this above.
# Get peak positions and heights, in order of highest to lowest peak.
peak_positions, peak_heights = rt.get_peak_statistics(
1. / rotate.freq, rotate.power)
print(peak_positions[0])
# Get peak positions and heights, in order of highest to lowest peak.
acf_peak_positions, acf_peak_heights = rt.get_peak_statistics(
rotate.lags, rotate.acf, sort_by="height")
print(acf_peak_positions[0])
# Get peak positions and heights, in order of lags.
acf_peak_positions, acf_peak_heights = rt.get_peak_statistics(
rotate.lags, rotate.acf, sort_by="position")
print(acf_peak_positions[0])
def test_acf():
time = np.linspace(0, 100, 1000)
p = 10
w = 2 * np.pi / p
flux = np.sin(w * time) + np.random.randn(len(time)) * 1e-2
flux_err = np.ones_like(flux) * 1e-2
rotate = ss.RotationModel(time, flux, flux_err)
acf_period = rotate.acf_rotation(interval=0.02043365)
assert np.isclose(acf_period, 10, atol=1)
acf_period = rotate.acf_rotation(interval=0.02043365, cutoff=1)
assert np.isclose(acf_period, 10, atol=1)
filtered_acf = rotate.acf
acf_period = rotate.acf_rotation(interval=0.02043365,
cutoff=1,
window_length=None)
unfiltered_acf = rotate.acf
assert np.isclose(acf_period, 10, atol=1)
def test_big_plot():
# Generate some data
time = np.linspace(0, 100, 1000)
p = 10
w = 2*np.pi/p
flux = np.sin(w*time) + np.random.randn(len(time))*1e-2
flux_err = np.ones_like(flux)*1e-2
rotate = ss.RotationModel(time, flux, flux_err)
ls_period = rotate.ls_rotation()
acf_period = rotate.acf_rotation(interval=0.02043365)
pdm_period, period_err = rotate.pdm_rotation(rotate.lags, pdm_nbins=10)
fig1 = rotate.big_plot()
fig1.savefig("big_plot_test")
fig2 = rotate.big_plot(methods=["ls", "acf"])
fig2.savefig("big_plot_test2")
fig3 = rotate.big_plot(methods=["ls", "acf", "pdm"])
fig3.savefig("big_plot_test3")
def test_uncertainty():
# Generate some data
np.random.seed(42)
t = np.linspace(0, 100, 1000)
p = 10
w = 2 * np.pi / p
x = np.sin(w * t) + np.random.randn(len(t)) * 1e-2
xerr = np.ones_like(x) * 1e-2
rm = ss.RotationModel(t, x, xerr)
nperiods = 200
period_grid = np.linspace(1, 20, nperiods)
pdm_period, period_err = rm.pdm_rotation(period_grid)
print(pdm_period, period_err)
fig = rm.pdm_plot()
plt.savefig("pdm_test")
# 2 day period
np.random.seed(42)
p = 2
w = 2 * np.pi / p
x = np.sin(w * t) + np.random.randn(len(t)) * 1e-2
xerr = np.ones_like(x) * 1e-2
period_grid = np.linspace(.1, 5, nperiods)
rm = ss.RotationModel(t, x, xerr)
pdm_period, period_err = rm.pdm_rotation(period_grid)
print(pdm_period, period_err)
fig = rm.pdm_plot()
plt.savefig("pdm_test_2")
# 5 day period
np.random.seed(42)
p = 5
w = 2 * np.pi / p
x = np.sin(w * t) + np.random.randn(len(t)) * 1e-2
xerr = np.ones_like(x) * 1e-2
period_grid = np.linspace(.1, 10, nperiods)
rm = ss.RotationModel(t, x, xerr)
pdm_period, period_err = rm.pdm_rotation(period_grid)
print(pdm_period, period_err)
fig = rm.pdm_plot()
plt.savefig("pdm_test_5")
# 20 day period
p = 20
np.random.seed(42)
w = 2 * np.pi / p
x = np.sin(w * t) + np.random.randn(len(t)) * 1e-2
xerr = np.ones_like(x) * 1e-2
period_grid = np.linspace(5, 30, nperiods)
rm = ss.RotationModel(t, x, xerr)
pdm_period, period_err = rm.pdm_rotation(period_grid)
print(pdm_period, period_err)
fig = rm.pdm_plot()
plt.savefig("pdm_test_20")
# 50 day period
np.random.seed(42)
t = np.linspace(0, 100, 1000)
p = 50
w = 2 * np.pi / p
x = np.sin(w * t) + np.random.randn(len(t)) * 1e-2
xerr = np.ones_like(x) * 1e-2
period_grid = 10**np.linspace(0, np.log10(200), nperiods)
rm = ss.RotationModel(t, x, xerr)
pdm_period, period_err = rm.pdm_rotation(period_grid)
print(pdm_period, period_err)
fig = rm.pdm_plot()
plt.savefig("pdm_test_50")
# 100 day period
np.random.seed(42)
p = 100
w = 2 * np.pi / p
x = np.sin(w * t) + np.random.randn(len(t)) * 1e-2
xerr = np.ones_like(x) * 1e-2
period_grid = 10**np.linspace(0, np.log10(200), nperiods)
rm = ss.RotationModel(t, x, xerr)
pdm_period, period_err = rm.pdm_rotation(period_grid)
print(pdm_period, period_err)
fig = rm.pdm_plot()
plt.savefig("pdm_test_100")
df = pd.read_csv("../kepler_stars_in_14_and_15.csv")
N = 4
cpm_periods = np.zeros(len(df))
for i, ticid in enumerate(df.TIC.values[:N]):
print("Measuring period for", ticid)
if os.path.exists("../data/{}_lc.csv".format(ticid)):
lc = pd.read_csv("../data/{}_lc.csv".format(ticid))
time, flux, flux_err = lc.time * 1, lc.flux * 1, lc.flux_err * 1
# Take off a straight line
p = np.polyfit(time, flux, 2)
flux -= np.polyval(p, time)
star = ss.RotationModel(time, flux, flux_err)
ls_period = star.ls_rotation(max_period=50)
cpm_periods[i] = ls_period
fig = star.big_plot(["ls"], method_xlim=(0, 50))
fig.savefig("plots/{}_bigplot".format(ticid))
plt.close()
else:
print("No light curve found")
xs = np.linspace(min(df.Prot.values[:N]), max(df.Prot.values[:N]), 100)
plt.plot(df.Prot.values[:N], cpm_periods[:N], ".")
plt.plot(xs, xs)
plt.savefig("kepler_period_comparison")
df["cpm_period"] = np.array(cpm_periods)