def test_k2_attributes():
"""
"""
# test inherited attributes
s = K2(epicid=EPICID, campaign=CAMPAIGN)
assert s.epicid is not None
assert s.target_coord is not None
gaia_params = s.query_gaia_dr2_catalog(return_nearest_xmatch=True)
assert isinstance(gaia_params, pd.Series)
tic_params = s.query_tic_catalog(return_nearest_xmatch=True)
assert isinstance(tic_params, pd.Series)
def test_k2_plots():
fig = K2.plot_everest_k2sff_comparison(EPICID)
assert isinstance(fig, Figure)
def test_k2_methods():
lcs = K2.get_lightcurves(EPICID)
# assert isinstance(lcs, lk.LightCurve)
assert isinstance(lcs, _KeplerLightCurve)
# -*- coding: utf-8 -*-
"""
test methods of k2 module
"""
import pandas as pd
import lightkurve as lk
from chronos.k2 import K2, Everest, K2sff, _KeplerLightCurve
from matplotlib.figure import Figure
# from matplotlib.axes import Axes
EPICID = 211916756 # k2-95
CAMPAIGN = 5 # or 18
s = K2(epicid=EPICID, campaign=CAMPAIGN)
def test_k2_attributes():
"""
"""
# test inherited attributes
assert s.epicid is not None
assert s.target_coord is not None
gaia_params = s.query_gaia_dr2_catalog(return_nearest_xmatch=True)
assert isinstance(gaia_params, pd.Series)
tic_params = s.query_tic_catalog(return_nearest_xmatch=True)
assert isinstance(tic_params, pd.Series)
def test_k2_lc_pipeline():
assert isinstance(s.lc_sap, lk.LightCurve)
def test_k2_tpf():
s = K2(epicid=EPICID, campaign=CAMPAIGN)
tpf = s.get_tpf()
assert isinstance(tpf, lk.targetpixelfile.TargetPixelFile)
def test_k2_lc_pipeline():
s = K2(epicid=EPICID, campaign=CAMPAIGN)
s.get_lc("sap")
assert isinstance(s.lc_sap, lk.LightCurve)
s.get_lc("pdcsap")
assert isinstance(s.lc_pdcsap, lk.LightCurve)
def save_ini_johannes(
self,
outdir=".",
save_lc=False,
cadence="short",
# pipeline="everest",
lctype="pdcsap",
teff=None,
logg=None,
feh=None,
rstar=None,
period=None,
epoch=None,
duration=None,
phase_trim=0.12,
):
"""target.ini file for johannes lcfit script
Parameters
----------
outdir : str
save_lc : bool (default=False)
save raw lcs in each sector needed to run johannes' lcfit script
and folded lcs in each sector and candidate to run vespa's calc_fpp
cadence : str
(default='short')
lctype : str
(default='pdcsap')
teff : tuple
logg : tuple
feh : tuple
rstar : tuple
period : float
orbital period [d]
epoch : float
[BJD]
duration : float
transit duration [d]
Example file
------------
[planets]
[[251319382.03]]
per = 3.708550700
t0 = 2458102.387129
t14 = 0.06
rprs = 0.0140929
[[251319382.02]]
[star]
teff = 5875, 100
logg = 4.92, 0.08
feh = 0.0, 0.1
rad = 0.95, 0.05
"""
# errmsg = "This method is available for TESS mission"
# assert self.mission=='tess', errmsg
if teff is not None:
assert isinstance(teff, tuple)
if logg is not None:
assert isinstance(logg, tuple)
if feh is not None:
assert isinstance(feh, tuple)
if rstar is not None:
assert isinstance(rstar, tuple)
target_name = self.target_name.replace(" ", "")
outdir = target_name if outdir == "." else outdir
if self.mission == "tess":
Teff = self.toi_Teff if teff is None else teff[0]
Teff_err = self.toi_Teff_err if teff is None else teff[1]
log10g = self.toi_logg if logg is None else logg[0]
log10g_err = self.toi_logg_err if logg is None else logg[1]
metfeh = self.toi_feh if feh is None else feh[0]
metfeh_err = self.toi_feh_err if feh is None else feh[1]
Rstar = self.toi_Rstar if rstar is None else rstar[0]
Rstar_err = self.toi_Rstar_err if rstar is None else rstar[1]
# get TOI candidate ephemerides
tois = get_tois(clobber=False)
df = tois[tois["TIC ID"] == self.ticid]
nplanets = len(df)
errmsg = f"{target_name} has {nplanets}"
# if period is not None:
# assert len(period) == nplanets, errmsg
# if epoch is not None:
# assert len(epoch) == nplanets, errmsg
# if duration is not None:
# assert len(duration) == nplanets, errmsg
lcs = []
if save_lc:
if cadence == "short":
kernel_size = 501
print(f"Querying {cadence} cadence PDCSAP light curve")
sc = ShortCadence(ticid=self.ticid)
for sector in tqdm(self.all_sectors):
try:
lc = sc.get_lc(lctype=lctype, sector=sector)
# BTJD
lc.time = lc.time + TESS_TIME_OFFSET
lcname = (
f"{target_name}-{lctype}-s{sector}-raw.txt")
lcpath = Path(outdir, lcname)
# for johannes
lc.to_csv(
lcpath,
columns=["time", "flux"],
header=False,
sep=" ",
index=False,
)
lcs.append(lc)
print("Saved: ", lcpath)
except Exception as e:
print(
f"Error: Cannot save {cadence} cadence light curve\n{e}"
)
else:
kernel_size = 49
errmsg = "Only cadence='short' is available for now."
raise ValueError(errmsg)
else:
lcname = (
f"{target_name}-{lctype}-s{self.all_sectors[0]}-raw.txt")
else:
kernel_size = 49
if teff is None:
errmsg = "Provide `teff`, `logg`, `feh`"
raise ValueError(errmsg)
Teff, Teff_err = teff
log10g, log10g_err = logg
metfeh, metfeh_err = feh
Rstar, Rstar_err = rstar
lcs = []
if save_lc:
sc = K2(epicid=self.epicid)
for camp in tqdm(self.all_campaigns):
try:
lc = sc.get_lc(lctype=lctype, campaign=camp)
lcname = f"{target_name}-{lctype}-c{camp}-raw.txt"
lcpath = Path(outdir, lcname)
# for johannes
lc.to_csv(
lcpath,
columns=["time", "flux"],
header=False,
sep=" ",
index=False,
)
lcs.append(lc)
print("Saved: ", lcpath)
except Exception as e:
print(
f"Error: Cannot save {cadence} cadence light curve\n{e}"
)
else:
lcname = (
f"{target_name}-{lctype}-s{self.all_campaigns[0]}-raw.txt")
out = f"#command: lcfit -b {self.mission.upper()[0]} -i {lcname} -c {target_name}.ini "
out += f"-o johannes --mcmc-steps 1000 --mcmc-burn 500 --mcmc-thin 10 -k {kernel_size}"
self.lcfit_cmd = out
out += "\n[planets]\n"
if self.mission == "tess":
for i, (k, row) in enumerate(df.iterrows()):
d = row
per = d["Period (days)"] if period is None else period[i]
t0 = d["Epoch (BJD)"] if epoch is None else epoch[i]
dur = (d["Duration (hours)"] /
24 if duration is None else duration[i])
if save_lc and self.mission == "tess":
for lc in lcs:
# save folded lc for vespa
# TODO: merge all sectors in one folded lc
flat = sc.get_flat_lc(
lc,
period=per,
epoch=t0,
duration=dur * 24,
window_length=5 * dur,
edge_cutoff=0.1,
)
flat = flat.remove_nans()
# flat_lcs.append(flat)
lcname2 = f"{target_name}-0{i+1}-{lctype}-s{lc.sector}-flat.txt"
lcpath2 = Path(outdir, lcname2)
flat.to_csv(
lcpath2,
columns=["time", "flux"],
header=False,
sep=" ",
index=False,
)
print("Saved: ", lcpath2)
fold = flat.fold(period=per, t0=t0).remove_nans()
lcname3 = f"{target_name}-0{i+1}-{lctype}-s{lc.sector}-fold.txt"
lcpath3 = Path(outdir, lcname3)
# clip folded lc near phase=0
idx = (fold.phase > -phase_trim) & (fold.phase <
phase_trim)
fold[idx].to_csv(
lcpath3,
columns=["time", "flux"],
header=False,
sep=" ",
index=False,
)
print("Saved: ", lcpath3)
print(f"==={d.TOI}===")
out += f"\t[[{d.TOI}]]\n"
out += f"\t\tper = {per:.6f}\n"
out += f"\t\tt0 = {t0:.6f}\n"
out += f"\t\tt14 = {dur:.2f}\n"
else:
raise NotImplementedError()
out += "[star]\n"
out += f"\tteff = {Teff:.0f}, {Teff_err:.0f}\n"
out += f"\tlogg = {log10g:.2f}, {log10g_err:.2f}\n"
out += f"\tfeh = {metfeh:.2f}, {metfeh_err:.2f}\n"
out += f"\trad = {Rstar:.2f}, {Rstar_err:.2f}\n"
outpath = Path(outdir, f"{target_name}.ini")
if not Path(outdir).exists():
Path(outdir).mkdir()
with open(outpath, "w") as file:
file.write(out)
print(f"Saved: {outpath}\n{out}")