def modelshift_weaksec(koi):
"""
Max secondary depth based on model-shift secondary test from Jeff Coughlin
secondary metric: D_sec_dv * (1 + 3*F_red_dv / sig_sec_dv)
"""
num = KOIDATA.ix[ku.koiname(koi), 'koi_tce_plnt_num']
if np.isnan(num):
num = 1
kid = KOIDATA.ix[ku.koiname(koi), 'kepid']
tce = '{:09.0f}-{:02.0f}'.format(kid, num)
#return largest depth between DV detrending and alternate detrending
try:
r = ROBOVETDATA.ix[tce]
except KeyError:
raise NoWeakSecondaryError(koi)
depth_dv = r['D_sec_dv'] * (1 + 3 * r['F_red_dv'] / r['sig_sec_dv'])
depth_alt = r['D_sec_alt'] * (1 + 3 * r['F_red_alt'] / r['sig_sec_alt'])
logging.debug(r[['D_sec_dv', 'F_red_dv', 'sig_sec_dv']])
logging.debug(r[['D_sec_alt', 'F_red_alt', 'sig_sec_alt']])
if np.isnan(depth_dv) and np.isnan(depth_alt):
#return weaksec_vv2(koi)
raise NoWeakSecondaryError(koi)
elif np.isnan(depth_dv):
return depth_alt
elif np.isnan(depth_alt):
return depth_dv
else:
return max(depth_dv, depth_alt)
def fpp_config(koi, **kwargs):
"""returns config object for given KOI
"""
folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))
if not os.path.exists(folder):
os.makedirs(folder)
config = ConfigObj(os.path.join(folder, "fpp.ini"))
koi = ku.koiname(koi)
rowefit = jrowe_fit(koi)
config["name"] = koi
ra, dec = ku.radec(koi)
config["ra"] = ra
config["dec"] = dec
config["rprs"] = rowefit.ix["RD1", "val"]
config["period"] = rowefit.ix["PE1", "val"]
config["starfield"] = kepler_starfield_file(koi)
for kw, val in kwargs.items():
config[kw] = val
config["constraints"] = {}
config["constraints"]["maxrad"] = default_r_exclusion(koi)
try:
config["constraints"]["secthresh"] = pipeline_weaksec(koi)
except NoWeakSecondaryError:
pass
return config
def fpp_config(koi, **kwargs):
"""returns config object for given KOI
"""
folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))
if not os.path.exists(folder):
os.makedirs(folder)
config = ConfigObj(os.path.join(folder, 'fpp.ini'))
koi = ku.koiname(koi)
rowefit = jrowe_fit(koi)
config['name'] = koi
ra, dec = ku.radec(koi)
config['ra'] = ra
config['dec'] = dec
config['rprs'] = rowefit.ix['RD1', 'val']
config['period'] = rowefit.ix['PE1', 'val']
config['starfield'] = kepler_starfield_file(koi)
for kw, val in kwargs.items():
config[kw] = val
config['constraints'] = {}
config['constraints']['maxrad'] = default_r_exclusion(koi)
try:
config['constraints']['secthresh'] = pipeline_weaksec(koi)
except NoWeakSecondaryError:
pass
return config
def weaksec_vv2(koi):
try:
weaksec = WEAKSECDATA.ix[ku.koiname(koi)]
secthresh = (weaksec["depth"] + 3 * weaksec["e_depth"]) * 1e-6
if weaksec["depth"] <= 0:
raise KeyError
except KeyError:
koi = ku.koiname(koi)
secthresh = 10 * KOIDATA.ix[koi, "koi_depth_err1"] * 1e-6
if np.isnan(secthresh):
secthresh = KOIDATA.ix[koi, "koi_depth"] / 2 * 1e-6
logging.warning(
"No (or bad) weak secondary info for {}, and no reported depth error. Defaulting to 1/2 reported depth = {}".format(
koi, secthresh
)
)
else:
logging.warning(
"No (or bad) weak secondary info for {}. Defaulting to 10x reported depth error = {}".format(
koi, secthresh
)
)
if np.isnan(secthresh):
raise NoWeakSecondaryError(koi)
return secthresh
def fpp_config(koi, **kwargs):
"""returns config object for given KOI
"""
folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))
if not os.path.exists(folder):
os.makedirs(folder)
config = ConfigObj(os.path.join(folder,'fpp.ini'))
koi = ku.koiname(koi)
rowefit = jrowe_fit(koi)
config['name'] = koi
ra,dec = ku.radec(koi)
config['ra'] = ra
config['dec'] = dec
config['rprs'] = rowefit.ix['RD1','val']
config['period'] = rowefit.ix['PE1', 'val']
config['starfield'] = kepler_starfield_file(koi)
for kw,val in kwargs.items():
config[kw] = val
config['constraints'] = {}
config['constraints']['maxrad'] = default_r_exclusion(koi)
try:
config['constraints']['secthresh'] = pipeline_weaksec(koi)
except NoWeakSecondaryError:
pass
return config
def modelshift_weaksec(koi):
"""
Max secondary depth based on model-shift secondary test from Jeff Coughlin
secondary metric: D_sec_dv * (1 + 3*F_red_dv / sig_sec_dv)
"""
num = ku.DATA.ix[ku.koiname(koi), 'koi_tce_plnt_num']
if np.isnan(num):
num = 1
kid = ku.DATA.ix[ku.koiname(koi), 'kepid']
tce = '{:09.0f}-{:02.0f}'.format(kid,num)
#return largest depth between DV detrending and alternate detrending
try:
r = ROBOVETDATA.ix[tce]
except KeyError:
raise NoWeakSecondaryError(koi)
depth_dv = r['D_sec_dv'] * (1 + 3*r['F_red_dv'] / r['sig_sec_dv'])
depth_alt = r['D_sec_alt'] * (1 + 3*r['F_red_alt'] / r['sig_sec_alt'])
logging.debug(r[['D_sec_dv','F_red_dv','sig_sec_dv']])
logging.debug(r[['D_sec_alt','F_red_alt','sig_sec_alt']])
if np.isnan(depth_dv) and np.isnan(depth_alt):
#return weaksec_vv2(koi)
raise NoWeakSecondaryError(koi)
elif np.isnan(depth_dv):
return depth_alt
elif np.isnan(depth_alt):
return depth_dv
else:
return max(depth_dv, depth_alt)
def star_config(koi,
bands=['g', 'r', 'i', 'z', 'J', 'H', 'K'],
unc=dict(g=0.05,
r=0.05,
i=0.05,
z=0.05,
J=0.02,
H=0.02,
K=0.02),
**kwargs):
"""returns star config object for given KOI
"""
folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))
if not os.path.exists(folder):
os.makedirs(folder)
config = ConfigObj(os.path.join(folder, 'star.ini'))
koi = ku.koiname(koi)
maxAV = koi_maxAV(koi)
config['maxAV'] = maxAV
mags = ku.KICmags(koi)
for band in bands:
if not np.isnan(mags[band]):
config[band] = (mags[band], unc[band])
config['Kepler'] = mags['Kepler']
kepid = KOIDATA.ix[koi, 'kepid']
try:
m = re.match('SPE', kicu.DATA.ix[kepid, 'teff_prov'])
except KeyError:
raise MissingStellarError('{} not in stellar table?'.format(kepid))
if m:
teff, e_teff = (kicu.DATA.ix[kepid, 'teff'], kicu.DATA.ix[kepid,
'teff_err1'])
if not any(np.isnan([teff, e_teff])):
config['Teff'] = (teff, e_teff)
feh, e_feh = (kicu.DATA.ix[kepid, 'feh'], kicu.DATA.ix[kepid,
'feh_err1'])
if not any(np.isnan([feh, e_feh])):
config['feh'] = (feh, e_feh)
try:
logg, e_logg = (kicu.DATA.ix[kepid,
'logg'], kicu.DATA.ix[kepid,
'logg_err1'])
if not any(np.isnan([logg, e_logg])):
config['logg'] = (logg, e_logg)
except:
pass
for kw, val in kwargs.items():
config[kw] = val
return config
def jrowe_fit_old(koi):
koinum = koiname(koi, star=True, koinum=True)
folder = os.path.join(JROWE_DIR, 'koi{}.n'.format(koinum))
num = np.round(koiname(koi,koinum=True) % 1 * 100)
fitfile = os.path.join(folder, 'n{:.0f}.dat'.format(num))
logging.debug('JRowe fitfile: {}'.format(fitfile))
return pd.read_table(fitfile,index_col=0,usecols=(0,1,3),
names=['par','val','a','err','c'],
delimiter='\s+')
def jrowe_fit_old(koi):
koinum = koiname(koi, star=True, koinum=True)
folder = os.path.join(JROWE_DIR, "koi{}.n".format(koinum))
num = np.round(koiname(koi, koinum=True) % 1 * 100)
fitfile = os.path.join(folder, "n{:.0f}.dat".format(num))
logging.debug("JRowe fitfile: {}".format(fitfile))
return pd.read_table(
fitfile, index_col=0, usecols=(0, 1, 3), names=["par", "val", "a", "err", "c"], delimiter="\s+"
)
def jrowe_fit_old(koi):
koinum = koiname(koi, star=True, koinum=True)
folder = os.path.join(JROWE_DIR, 'koi{}.n'.format(koinum))
num = np.round(koiname(koi,koinum=True) % 1 * 100)
fitfile = os.path.join(folder, 'n{:.0f}.dat'.format(num))
logging.debug('JRowe fitfile: {}'.format(fitfile))
return pd.read_table(fitfile,index_col=0,usecols=(0,1,3),
names=['par','val','a','err','c'],
delimiter='\s+')
def star_config(koi, bands=['g','r','i','z','J','H','K'],
unc=dict(g=0.05, r=0.05, i=0.05, z=0.05,
J=0.02, H=0.02, K=0.02), **kwargs):
"""returns star config object for given KOI
"""
folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))
if not os.path.exists(folder):
os.makedirs(folder)
config = ConfigObj(os.path.join(folder,'star.ini'))
koi = ku.koiname(koi)
maxAV = koi_maxAV(koi)
config['maxAV'] = maxAV
mags = ku.KICmags(koi)
for band in bands:
if not np.isnan(mags[band]):
config[band] = (mags[band], unc[band])
config['Kepler'] = mags['Kepler']
kepid = ku.DATA.ix[koi,'kepid']
try:
m = re.match('SPE', kicu.DATA.ix[kepid, 'teff_prov'])
except KeyError:
raise MissingStellarError('{} not in stellar table?'.format(kepid))
if m:
teff, e_teff = (kicu.DATA.ix[kepid, 'teff'],
kicu.DATA.ix[kepid, 'teff_err1'])
if not any(np.isnan([teff, e_teff])):
config['Teff'] = (teff, e_teff)
feh, e_feh = (kicu.DATA.ix[kepid, 'feh'],
kicu.DATA.ix[kepid, 'feh_err1'])
if not any(np.isnan([feh, e_feh])):
config['feh'] = (feh, e_feh)
try:
logg, e_logg = (kicu.DATA.ix[kepid, 'logg'],
kicu.DATA.ix[kepid, 'logg_err1'])
if not any(np.isnan([logg, e_logg])):
config['logg'] = (logg, e_logg)
except:
pass
for kw,val in kwargs.items():
config[kw] = val
return config
def star_config(
koi,
bands=["g", "r", "i", "z", "J", "H", "K"],
unc=dict(g=0.05, r=0.05, i=0.05, z=0.05, J=0.02, H=0.02, K=0.02),
**kwargs
):
"""returns star config object for given KOI
"""
folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))
if not os.path.exists(folder):
os.makedirs(folder)
config = ConfigObj(os.path.join(folder, "star.ini"))
koi = ku.koiname(koi)
maxAV = koi_maxAV(koi)
config["maxAV"] = maxAV
mags = ku.KICmags(koi)
for band in bands:
if not np.isnan(mags[band]):
config[band] = (mags[band], unc[band])
config["Kepler"] = mags["Kepler"]
kepid = KOIDATA.ix[koi, "kepid"]
try:
m = re.match("SPE", kicu.DATA.ix[kepid, "teff_prov"])
except KeyError:
raise MissingStellarError("{} not in stellar table?".format(kepid))
if m:
teff, e_teff = (kicu.DATA.ix[kepid, "teff"], kicu.DATA.ix[kepid, "teff_err1"])
if not any(np.isnan([teff, e_teff])):
config["Teff"] = (teff, e_teff)
feh, e_feh = (kicu.DATA.ix[kepid, "feh"], kicu.DATA.ix[kepid, "feh_err1"])
if not any(np.isnan([feh, e_feh])):
config["feh"] = (feh, e_feh)
try:
logg, e_logg = (kicu.DATA.ix[kepid, "logg"], kicu.DATA.ix[kepid, "logg_err1"])
if not any(np.isnan([logg, e_logg])):
config["logg"] = (logg, e_logg)
except:
pass
for kw, val in kwargs.items():
config[kw] = val
return config
def koi_maxAV(koi):
try:
maxAV = MAXAV.ix[ku.koiname(koi), 'maxAV']
except KeyError:
ra, dec = ku.radec(koi)
maxAV = get_AV_infinity(ra, dec)
return maxAV
def setup_fpp(koi,
bands=['g', 'r', 'i', 'z', 'J', 'H', 'K'],
unc=dict(g=0.05, r=0.05, i=0.05, z=0.05, J=0.02, H=0.02, K=0.02),
star_kws=None,
fpp_kws=None,
trsig_kws=None,
trsig_overwrite=False,
star_only=False,
fpp_only=False):
if star_kws is None:
star_kws = {}
if fpp_kws is None:
fpp_kws = {}
if trsig_kws is None:
trsig_kws = {}
if not star_only:
#save transit signal
folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))
trsig_file = os.path.join(folder, 'trsig.pkl')
if os.path.exists(trsig_file):
if os.path.getsize(trsig_file) == 0:
os.remove(trsig_file)
if not os.path.exists(trsig_file) or\
trsig_overwrite:
sig = JRowe_KeplerTransitSignal(koi, refit_mcmc=True, **trsig_kws)
sig.save(os.path.join(folder, 'trsig.pkl'))
fpp = fpp_config(koi, **fpp_kws)
fpp.write()
if not fpp_only:
star = star_config(koi, bands=bands, unc=unc, **star_kws)
star.write()
def jrowe_fit(koi):
koi = koiname(koi)
pars = ['RHO', 'EP1','PE1', 'BB1', 'RD1']
vals = list(JROWE_DATA.ix[koi,['rhostar','T0','P','b','rdr']])
return pd.DataFrame({'val':vals}, index=pars)
def koi_propdist(koi, prop):
"""
"""
koi = ku.koiname(koi)
kepid = ku.DATA.ix[koi, 'kepid']
try:
#first try cumulative table
val = ku.DATA.ix[koi, prop]
u1 = ku.DATA.ix[koi, prop + '_err1']
u2 = ku.DATA.ix[koi, prop + '_err2']
except KeyError:
try:
#try Huber table
val = kicu.DATA.ix[kepid, prop]
u1 = kicu.DATA.ix[kepid, prop + '_err1']
u2 = kicu.DATA.ix[kepid, prop + '_err2']
except KeyError:
raise NoStellarPropError(koi)
if np.isnan(val) or np.isnan(u2) or np.isnan(u1):
raise MissingStellarPropError('{}: {} = ({},{},{})'.format(
koi, prop, val, u1, u2))
try:
return dists.fit_doublegauss(val, -u2, u1)
except:
raise StellarPropError('{}: {} = ({},{},{})'.format(
koi, prop, val, u1, u2))
def setup_fpp(koi, bands=['g','r','i','z','J','H','K'],
unc=dict(g=0.05, r=0.05, i=0.05, z=0.05,
J=0.02, H=0.02, K=0.02),
star_kws=None, fpp_kws=None, trsig_kws=None,
trsig_overwrite=False,
star_only=False, fpp_only=False):
if star_kws is None:
star_kws = {}
if fpp_kws is None:
fpp_kws = {}
if trsig_kws is None:
trsig_kws = {}
if not star_only:
#save transit signal
folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))
trsig_file = os.path.join(folder,'trsig.pkl')
if os.path.exists(trsig_file):
if os.path.getsize(trsig_file)==0:
os.remove(trsig_file)
if not os.path.exists(trsig_file) or\
trsig_overwrite:
sig = JRowe_KeplerTransitSignal(koi, refit_mcmc=True,
**trsig_kws)
sig.save(os.path.join(folder,'trsig.pkl'))
fpp = fpp_config(koi, **fpp_kws)
fpp.write()
if not fpp_only:
star = star_config(koi, bands=bands, unc=unc, **star_kws)
star.write()
def koi_maxAV(koi):
try:
maxAV = MAXAV.ix[ku.koiname(koi),'maxAV']
except KeyError:
ra,dec = ku.radec(koi)
maxAV = get_AV_infinity(ra,dec)
return maxAV
def koi_propdist(koi, prop):
"""
"""
koi = ku.koiname(koi)
kepid = ku.DATA.ix[koi, 'kepid']
try:
#first try cumulative table
val = ku.DATA.ix[koi, prop]
u1 = ku.DATA.ix[koi, prop+'_err1']
u2 = ku.DATA.ix[koi, prop+'_err2']
except KeyError:
try:
#try Huber table
val = kicu.DATA.ix[kepid, prop]
u1 = kicu.DATA.ix[kepid, prop+'_err1']
u2 = kicu.DATA.ix[kepid, prop+'_err2']
except KeyError:
raise NoStellarPropError(koi)
if np.isnan(val) or np.isnan(u2) or np.isnan(u1):
raise MissingStellarPropError('{}: {} = ({},{},{})'.format(koi,
prop,
val,u1,u2))
try:
return dists.fit_doublegauss(val, -u2, u1)
except:
raise StellarPropError('{}: {} = ({},{},{})'.format(koi,
prop,
val,u1,u2))
def star_config(koi, bands=['g','r','i','z','J','H','K'],
unc=dict(g=0.05, r=0.05, i=0.05, z=0.05,
J=0.02, H=0.02, K=0.02), **kwargs):
"""returns star config object for given KOI
"""
folder = os.path.join(KOI_FPPDIR, ku.koiname(koi))
if not os.path.exists(folder):
os.makedirs(folder)
config = ConfigObj(os.path.join(folder,'star.ini'))
koi = ku.koiname(koi)
maxAV = koi_maxAV(koi)
config['maxAV'] = maxAV
mags = ku.KICmags(koi)
for band in bands:
if not np.isnan(mags[band]):
config[band] = (mags[band], unc[band])
config['Kepler'] = mags['Kepler']
kepid = KOIDATA.ix[koi,'kepid']
if use_property(kepid, 'teff'):
teff, e_teff = (kicu.DATA.ix[kepid, 'teff'],
kicu.DATA.ix[kepid, 'teff_err1'])
if not any(np.isnan([teff, e_teff])):
config['Teff'] = (teff, e_teff)
if use_property(kepid, 'logg'):
logg, e_logg = (kicu.DATA.ix[kepid, 'logg'],
kicu.DATA.ix[kepid, 'logg_err1'])
if not any(np.isnan([logg, e_logg])):
config['logg'] = (logg, e_logg)
if use_property(kepid, 'feh'):
feh, e_feh = (kicu.DATA.ix[kepid, 'feh'],
kicu.DATA.ix[kepid, 'feh_err1'])
if not any(np.isnan([feh, e_feh])):
config['feh'] = (feh, e_feh)
for kw,val in kwargs.items():
config[kw] = val
return config
def jrowe_fit(koi):
koi = koiname(koi)
pars = ['RHO', 'EP1','PE1', 'BB1', 'RD1']
vals = list(JROWE_DATA.ix[koi,['rhostar','T0','P','b','rdr']])
old = jrowe_fit_old(koi)
vals[1] = old.ix['EP1','val'] # hack to make sure epoch matches old photometry.
return pd.DataFrame({'val':vals}, index=pars)
def jrowe_fit(koi):
koi = koiname(koi)
pars = ["RHO", "EP1", "PE1", "BB1", "RD1"]
vals = list(JROWE_DATA.ix[koi, ["rhostar", "T0", "P", "b", "rdr"]])
old = jrowe_fit_old(koi)
vals[1] = old.ix["EP1", "val"] # hack to make sure epoch matches old photometry.
return pd.DataFrame({"val": vals}, index=pars)
def jrowe_fit(koi):
koi = koiname(koi)
pars = ['RHO', 'EP1', 'PE1', 'BB1', 'RD1']
vals = list(JROWE_DATA.ix[koi, ['rhostar', 'T0', 'P', 'b', 'rdr']])
old = jrowe_fit_old(koi)
vals[1] = old.ix['EP1',
'val'] # hack to make sure epoch matches old photometry.
return pd.DataFrame({'val': vals}, index=pars)
def weaksec_vv2(koi):
try:
raise KeyError # just skip this, as WEAKSECDATA is obselete
weaksec = WEAKSECDATA.ix[ku.koiname(koi)]
secthresh = (weaksec['depth'] + 3*weaksec['e_depth'])*1e-6
if weaksec['depth'] <= 0:
raise KeyError
except KeyError:
koi = ku.koiname(koi)
secthresh = 10*KOIDATA.ix[koi,'koi_depth_err1'] * 1e-6
if np.isnan(secthresh):
secthresh = KOIDATA.ix[koi,'koi_depth'] / 2 * 1e-6
logging.warning('No (or bad) weak secondary info for {}, and no reported depth error. Defaulting to 1/2 reported depth = {}'.format(koi, secthresh))
else:
logging.warning('No (or bad) weak secondary info for {}. Defaulting to 10x reported depth error = {}'.format(koi, secthresh))
if np.isnan(secthresh):
raise NoWeakSecondaryError(koi)
return secthresh
def __init__(self, koi, data_root=KPLR_ROOT):
self.koi = koiname(koi)
client = kplr.API(data_root=data_root)
koinum = koiname(koi, koinum=True)
k = client.koi(koinum)
#get all data
df = k.all_LCdata
time = np.array(df['TIME'])
flux = np.array(df['SAP_FLUX'])
err = np.array(df['SAP_FLUX_ERR'])
qual = np.array(df['SAP_QUALITY'])
m = np.isfinite(time) * np.isfinite(flux) * np.isfinite(err)
m *= qual == 0
time = time[m]
flux = flux[m]
err = err[m]
period = k.koi_period
epoch = k.koi_time0bk
duration = k.koi_duration
def __init__(self, koi, data_root=KPLR_ROOT):
self.koi = koiname(koi)
client = kplr.API(data_root=data_root)
koinum = koiname(koi, koinum=True)
k = client.koi(koinum)
#get all data
df = k.all_LCdata
time = np.array(df['TIME'])
flux = np.array(df['SAP_FLUX'])
err = np.array(df['SAP_FLUX_ERR'])
qual = np.array(df['SAP_QUALITY'])
m = np.isfinite(time)*np.isfinite(flux)*np.isfinite(err)
m *= qual==0
time = time[m]
flux = flux[m]
err = err[m]
period = k.koi_period
epoch = k.koi_time0bk
duration = k.koi_duration
def pipeline_weaksec(koi):
try:
weaksec = WEAKSECDATA.ix[ku.koiname(koi)]
secthresh = (weaksec['depth'] + 3*weaksec['e_depth'])*1e-6
if weaksec['depth'] <= 0:
raise KeyError
except KeyError:
secthresh = 10*ku.DATA.ix[koi,'koi_depth_err1'] * 1e-6
if np.isnan(secthresh):
secthresh = ku.DATA.ix[koi,'koi_depth'] / 2 * 1e-6
logging.warning('No (or bad) weak secondary info for {}, and no reported depth error. Defaulting to 1/2 reported depth = {}'.format(koi, secthresh))
else:
logging.warning('No (or bad) weak secondary info for {}. Defaulting to 10x reported depth error = {}'.format(koi, secthresh))
if np.isnan(secthresh):
raise NoWeakSecondaryError(koi)
return secthresh
def pipeline_weaksec(koi):
try:
weaksec = WEAKSECDATA.ix[ku.koiname(koi)]
secthresh = (weaksec['depth'] + 3 * weaksec['e_depth']) * 1e-6
if weaksec['depth'] <= 0:
raise KeyError
except KeyError:
secthresh = 10 * ku.DATA.ix[koi, 'koi_depth_err1'] * 1e-6
if np.isnan(secthresh):
secthresh = ku.DATA.ix[koi, 'koi_depth'] / 2 * 1e-6
logging.warning(
'No (or bad) weak secondary info for {}, and no reported depth error. Defaulting to 1/2 reported depth = {}'
.format(koi, secthresh))
else:
logging.warning(
'No (or bad) weak secondary info for {}. Defaulting to 10x reported depth error = {}'
.format(koi, secthresh))
if np.isnan(secthresh):
raise NoWeakSecondaryError(koi)
return secthresh
def __init__(self,
koi,
recalc=False,
use_JRowe=True,
trsig_kws=None,
tag=None,
starmodel_mcmc_kws=None,
**kwargs):
koi = koiname(koi)
#if saved popset exists, load
folder = os.path.join(KOI_FPPDIR, koi)
if tag is not None:
folder += '_{}'.format(tag)
if not os.path.exists(folder):
os.makedirs(folder)
if trsig_kws is None:
trsig_kws = {}
#first check if pickled signal is there to be loaded
trsigfile = os.path.join(folder, 'trsig.pkl')
if os.path.exists(trsigfile):
trsig = pickle.load(open(trsigfile, 'rb'))
else:
if use_JRowe:
trsig = JRowe_KeplerTransitSignal(koi, **trsig_kws)
else:
trsig = KeplerTransitSignal(koi, **trsig_kws)
popsetfile = os.path.join(folder, 'popset.h5')
if os.path.exists(popsetfile) and not recalc:
popset = PopulationSet(popsetfile, **kwargs)
else:
koinum = koiname(koi, koinum=True)
kepid = ku.DATA.ix[koi, 'kepid']
if 'mass' not in kwargs:
kwargs['mass'] = koi_propdist(koi, 'mass')
if 'radius' not in kwargs:
kwargs['radius'] = koi_propdist(koi, 'radius')
if 'feh' not in kwargs:
kwargs['feh'] = koi_propdist(koi, 'feh')
if 'age' not in kwargs:
try:
kwargs['age'] = koi_propdist(koi, 'age')
except:
kwargs['age'] = (9.7, 0.1) #default age
if 'Teff' not in kwargs:
kwargs['Teff'] = kicu.DATA.ix[kepid, 'teff']
if 'logg' not in kwargs:
kwargs['logg'] = kicu.DATA.ix[kepid, 'logg']
if 'rprs' not in kwargs:
if use_JRowe:
kwargs['rprs'] = trsig.rowefit.ix['RD1', 'val']
else:
kwargs['rprs'] = ku.DATA.ix[koi, 'koi_ror']
#if stellar properties are determined spectroscopically,
# fit stellar model
if 'starmodel' not in kwargs:
if re.match('SPE', kicu.DATA.ix[kepid, 'teff_prov']):
logging.info(
'Spectroscopically determined stellar properties.')
#first, see if there already is a starmodel to load
#fit star model
Teff = kicu.DATA.ix[kepid, 'teff']
e_Teff = kicu.DATA.ix[kepid, 'teff_err1']
logg = kicu.DATA.ix[kepid, 'logg']
e_logg = kicu.DATA.ix[kepid, 'logg_err1']
feh = kicu.DATA.ix[kepid, 'feh']
e_feh = kicu.DATA.ix[kepid, 'feh_err1']
logging.info(
'fitting StarModel (Teff=({},{}), logg=({},{}), feh=({},{}))...'
.format(Teff, e_Teff, logg, e_logg, feh, e_feh))
dar = Dartmouth_Isochrone()
starmodel = StarModel(dar,
Teff=(Teff, e_Teff),
logg=(logg, e_logg),
feh=(feh, e_feh))
if starmodel_mcmc_kws is None:
starmodel_mcmc_kws = {}
starmodel.fit_mcmc(**starmodel_mcmc_kws)
logging.info('Done.')
kwargs['starmodel'] = starmodel
if 'mags' not in kwargs:
kwargs['mags'] = ku.KICmags(koi)
if 'ra' not in kwargs:
kwargs['ra'], kwargs['dec'] = ku.radec(koi)
if 'period' not in kwargs:
kwargs['period'] = ku.DATA.ix[koi, 'koi_period']
if 'pl_kws' not in kwargs:
kwargs['pl_kws'] = {}
if 'fp_specific' not in kwargs['pl_kws']:
rp = kwargs['radius'].mu * kwargs['rprs'] * RSUN / REARTH
kwargs['pl_kws']['fp_specific'] = fp_fressin(rp)
#trilegal_filename = os.path.join(folder,'starfield.h5')
trilegal_filename = kepler_starfield_file(koi)
popset = PopulationSet(trilegal_filename=trilegal_filename,
**kwargs)
#popset.save_hdf('{}/popset.h5'.format(folder), overwrite=True)
lhoodcachefile = os.path.join(folder, 'lhoodcache.dat')
self.koi = koi
FPPCalculation.__init__(self, trsig, popset, folder=folder)
self.save()
self.apply_default_constraints()
def __init__(self, koi, recalc=False, use_JRowe=True, trsig_kws=None, tag=None, starmodel_mcmc_kws=None, **kwargs):
koi = koiname(koi)
# if saved popset exists, load
folder = os.path.join(KOI_FPPDIR, koi)
if tag is not None:
folder += "_{}".format(tag)
if not os.path.exists(folder):
os.makedirs(folder)
if trsig_kws is None:
trsig_kws = {}
# first check if pickled signal is there to be loaded
trsigfile = os.path.join(folder, "trsig.pkl")
if os.path.exists(trsigfile):
trsig = pickle.load(open(trsigfile, "rb"))
else:
if use_JRowe:
trsig = JRowe_KeplerTransitSignal(koi, **trsig_kws)
else:
trsig = KeplerTransitSignal(koi, **trsig_kws)
popsetfile = os.path.join(folder, "popset.h5")
if os.path.exists(popsetfile) and not recalc:
popset = PopulationSet(popsetfile, **kwargs)
else:
koinum = koiname(koi, koinum=True)
kepid = KOIDATA.ix[koi, "kepid"]
if "mass" not in kwargs:
kwargs["mass"] = koi_propdist(koi, "mass")
if "radius" not in kwargs:
kwargs["radius"] = koi_propdist(koi, "radius")
if "feh" not in kwargs:
kwargs["feh"] = koi_propdist(koi, "feh")
if "age" not in kwargs:
try:
kwargs["age"] = koi_propdist(koi, "age")
except:
kwargs["age"] = (9.7, 0.1) # default age
if "Teff" not in kwargs:
kwargs["Teff"] = kicu.DATA.ix[kepid, "teff"]
if "logg" not in kwargs:
kwargs["logg"] = kicu.DATA.ix[kepid, "logg"]
if "rprs" not in kwargs:
if use_JRowe:
kwargs["rprs"] = trsig.rowefit.ix["RD1", "val"]
else:
kwargs["rprs"] = KOIDATA.ix[koi, "koi_ror"]
# if stellar properties are determined spectroscopically,
# fit stellar model
if "starmodel" not in kwargs:
if re.match("SPE", kicu.DATA.ix[kepid, "teff_prov"]):
logging.info("Spectroscopically determined stellar properties.")
# first, see if there already is a starmodel to load
# fit star model
Teff = kicu.DATA.ix[kepid, "teff"]
e_Teff = kicu.DATA.ix[kepid, "teff_err1"]
logg = kicu.DATA.ix[kepid, "logg"]
e_logg = kicu.DATA.ix[kepid, "logg_err1"]
feh = kicu.DATA.ix[kepid, "feh"]
e_feh = kicu.DATA.ix[kepid, "feh_err1"]
logging.info(
"fitting StarModel (Teff=({},{}), logg=({},{}), feh=({},{}))...".format(
Teff, e_Teff, logg, e_logg, feh, e_feh
)
)
dar = Dartmouth_Isochrone()
starmodel = StarModel(dar, Teff=(Teff, e_Teff), logg=(logg, e_logg), feh=(feh, e_feh))
if starmodel_mcmc_kws is None:
starmodel_mcmc_kws = {}
starmodel.fit(**starmodel_mcmc_kws)
logging.info("Done.")
kwargs["starmodel"] = starmodel
if "mags" not in kwargs:
kwargs["mags"] = ku.KICmags(koi)
if "ra" not in kwargs:
kwargs["ra"], kwargs["dec"] = ku.radec(koi)
if "period" not in kwargs:
kwargs["period"] = KOIDATA.ix[koi, "koi_period"]
if "pl_kws" not in kwargs:
kwargs["pl_kws"] = {}
if "fp_specific" not in kwargs["pl_kws"]:
rp = kwargs["radius"].mu * kwargs["rprs"] * RSUN / REARTH
kwargs["pl_kws"]["fp_specific"] = fp_fressin(rp)
# trilegal_filename = os.path.join(folder,'starfield.h5')
trilegal_filename = kepler_starfield_file(koi)
popset = PopulationSet(trilegal_filename=trilegal_filename, **kwargs)
# popset.save_hdf('{}/popset.h5'.format(folder), overwrite=True)
lhoodcachefile = os.path.join(folder, "lhoodcache.dat")
self.koi = koi
FPPCalculation.__init__(self, trsig, popset, folder=folder)
self.save()
self.apply_default_constraints()
def __init__(self,koi,mcmc=True,maxslope=None,refit_mcmc=False,
**kwargs):
self.folder = '%s/koi%i.n' % (JROWE_DIR,
koiname(koi,star=True,
koinum=True))
num = np.round(koiname(koi,koinum=True) % 1 * 100)
self.lcfile = '%s/tremove.%i.dat' % (self.folder,num)
if (not os.path.exists(self.lcfile)) or (os.stat(self.lcfile)[6]==0):
kepid = ku.kepid(koi)
self.lcfile = '{}/klc{08.0f}.dct.dat'.format(self.folder,kepid)
if not os.path.exists(self.lcfile):
raise MissingKOIError('{} does not exist.'.format(self.lcfile))
if os.stat(self.lcfile)[6]==0:
raise EmptyPhotometryError('{} photometry file ({}) is empty'.format(koiname(koi),
self.lcfile))
logging.debug('Reading photometry from {}'.format(self.lcfile))
lc = pd.read_table(self.lcfile,names=['t','f','df'],
delimiter='\s+')
self.ttfile = '%s/koi%07.2f.tt' % (self.folder,koiname(koi,koinum=True))
self.has_ttvs = os.path.exists(self.ttfile)
if self.has_ttvs:
if os.stat(self.ttfile)[6]==0:
self.has_ttvs = False
logging.warning('TTV file exists for {}, but is empty. No TTVs applied.'.format(koiname(koi)))
else:
logging.debug('Reading transit times from {}'.format(self.ttfile))
tts = pd.read_table(self.ttfile,names=['tc','foo1','foo2'],delimiter='\s+')
#self.rowefitfile = '%s/n%i.dat' % (self.folder,num)
#self.rowefit = pd.read_table(self.rowefitfile,index_col=0,usecols=(0,1,3),
# names=['par','val','a','err','c'],
# delimiter='\s+')
self.rowefit = jrowe_fit(koi)
P = self.rowefit.ix['PE1','val']
RR = self.rowefit.ix['RD1','val']
aR = (self.rowefit.ix['RHO','val']*G*(P*DAY)**2/(3*np.pi))**(1./3)
cosi = self.rowefit.ix['BB1','val']/aR
Tdur = P*DAY/np.pi*np.arcsin(1/aR * (((1+RR)**2 - (aR*cosi)**2)/(1 - cosi**2))**(0.5))/DAY
if 1/aR * (((1+RR)**2 - (aR*cosi)**2)/(1 - cosi**2))**(0.5) > 1:
logging.warning('arcsin argument in Tdur calculation > 1; setting to 1 for purposes of rough Tdur calculation...')
Tdur = P*DAY/np.pi*np.arcsin(1)/DAY
if (1+RR) < (self.rowefit.ix['BB1','val']):
#Tdur = P*DAY/np.pi*np.arcsin(1/aR * (((1+RR)**2 - (aR*0)**2)/(1 - 0**2))**(0.5))/DAY/2.
raise BadRoweFitError('best-fit impact parameter ({:.2f}) inconsistent with best-fit radius ratio ({}).'.format(self.rowefit.ix['BB1','val'],RR))
if RR < 0:
raise BadRoweFitError('{0} has negative RoR ({1}) from JRowe MCMC fit'.format(koiname(koi),RR))
if RR > 1:
raise BadRoweFitError('{0} has RoR > 1 ({1}) from JRowe MCMC fit'.format(koiname(koi),RR))
if aR < 1:
raise BadRoweFitError('{} has a/Rstar < 1 ({}) from JRowe MCMC fit'.format(koiname(koi),aR))
self.P = P
self.aR = aR
self.Tdur = Tdur
self.epoch = self.rowefit.ix['EP1','val'] + 2504900
logging.debug('Tdur = {:.2f}'.format(self.Tdur))
logging.debug('aR={0}, cosi={1}, RR={2}'.format(aR,cosi,RR))
logging.debug('arcsin arg={}'.format(1/aR * (((1+RR)**2 - (aR*cosi)**2)/(1 - cosi**2))**(0.5)))
logging.debug('inside sqrt in arcsin arg={}'.format((((1+RR)**2 - (aR*cosi)**2)/(1 - cosi**2))))
logging.debug('best-fit impact parameter={:.2f}'.format(self.rowefit.ix['BB1','val']))
lc['t'] += (2450000+0.5)
lc['f'] += 1 # - self.rowefit.ix['ZPT','val']
if self.has_ttvs:
tts['tc'] += 2504900
ts = pd.Series()
fs = pd.Series()
dfs = pd.Series()
if self.has_ttvs:
for t0 in tts['tc']:
t = lc['t'] - t0
ok = np.absolute(t) < 2*self.Tdur
ts = ts.append(t[ok])
fs = fs.append(lc['f'][ok])
dfs = dfs.append(lc['df'][ok])
else:
center = self.epoch % self.P
t = np.mod(lc['t'] - center + self.P/2,self.P) - self.P/2
ok = np.absolute(t) < 2*self.Tdur
ts = t[ok]
fs = lc['f'][ok]
dfs = lc['df'][ok]
logging.debug('{0}: has_ttvs is {1}'.format(koi,self.has_ttvs))
logging.debug('{} light curve points used'.format(ok.sum()))
if maxslope is None:
#set maxslope using duration
maxslope = max(Tdur*24/0.5 * 2, 30) #hardcoded in transitFPP as default=30
p0 = [Tdur,RR**2,3,0]
self.p0 = p0
logging.debug('initial trapezoid parameters guess: {}'.format(p0))
TransitSignal.__init__(self,np.array(ts),np.array(fs),
np.array(dfs),p0=p0,
name=koiname(koi),
P=P,maxslope=maxslope)
if mcmc:
self.MCMC(refit=refit_mcmc)
if self.hasMCMC and not self.fit_converged:
logging.warning('Trapezoidal MCMC fit did not converge for {}.'.format(self.name))
def __init__(self,
koi,
mcmc=True,
maxslope=None,
refit_mcmc=False,
**kwargs):
self.folder = '%s/koi%i.n' % (JROWE_DIR,
koiname(koi, star=True, koinum=True))
num = np.round(koiname(koi, koinum=True) % 1 * 100)
self.lcfile = '%s/tremove.%i.dat' % (self.folder, num)
if not os.path.exists(self.lcfile):
raise MissingKOIError('{} does not exist.'.format(self.lcfile))
logging.debug('Reading photometry from {}'.format(self.lcfile))
#break if photometry file is empty
if os.stat(self.lcfile)[6] == 0:
raise EmptyPhotometryError(
'{} photometry file ({}) is empty'.format(
koiname(koi), self.lcfile))
lc = pd.read_table(self.lcfile,
names=['t', 'f', 'df'],
delimiter='\s+')
self.ttfile = '%s/koi%07.2f.tt' % (self.folder,
koiname(koi, koinum=True))
self.has_ttvs = os.path.exists(self.ttfile)
if self.has_ttvs:
if os.stat(self.ttfile)[6] == 0:
self.has_ttvs = False
logging.warning(
'TTV file exists for {}, but is empty. No TTVs applied.'.
format(koiname(koi)))
else:
logging.debug('Reading transit times from {}'.format(
self.ttfile))
tts = pd.read_table(self.ttfile,
names=['tc', 'foo1', 'foo2'],
delimiter='\s+')
self.rowefitfile = '%s/n%i.dat' % (self.folder, num)
self.rowefit = pd.read_table(self.rowefitfile,
index_col=0,
usecols=(0, 1, 3),
names=['par', 'val', 'a', 'err', 'c'],
delimiter='\s+')
logging.debug('JRowe fitfile: {}'.format(self.rowefitfile))
P = self.rowefit.ix['PE1', 'val']
RR = self.rowefit.ix['RD1', 'val']
aR = (self.rowefit.ix['RHO', 'val'] * G * (P * DAY)**2 /
(3 * np.pi))**(1. / 3)
cosi = self.rowefit.ix['BB1', 'val'] / aR
Tdur = P * DAY / np.pi * np.arcsin(1 / aR *
(((1 + RR)**2 - (aR * cosi)**2) /
(1 - cosi**2))**(0.5)) / DAY
if 1 / aR * (((1 + RR)**2 - (aR * cosi)**2) /
(1 - cosi**2))**(0.5) > 1:
logging.warning(
'arcsin argument in Tdur calculation > 1; setting to 1 for purposes of rough Tdur calculation...'
)
Tdur = P * DAY / np.pi * np.arcsin(1) / DAY
if (1 + RR) < (self.rowefit.ix['BB1', 'val']):
#Tdur = P*DAY/np.pi*np.arcsin(1/aR * (((1+RR)**2 - (aR*0)**2)/(1 - 0**2))**(0.5))/DAY/2.
raise BadRoweFitError(
'best-fit impact parameter ({:.2f}) inconsistent with best-fit radius ratio ({}).'
.format(self.rowefit.ix['BB1', 'val'], RR))
if RR < 0:
raise BadRoweFitError(
'{0} has negative RoR ({1}) from JRowe MCMC fit'.format(
koiname(koi), RR))
if RR > 1:
raise BadRoweFitError(
'{0} has RoR > 1 ({1}) from JRowe MCMC fit'.format(
koiname(koi), RR))
if aR < 1:
raise BadRoweFitError(
'{} has a/Rstar < 1 ({}) from JRowe MCMC fit'.format(
koiname(koi), aR))
self.P = P
self.aR = aR
self.Tdur = Tdur
self.epoch = self.rowefit.ix['EP1', 'val'] + 2504900
logging.debug('Tdur = {:.2f}'.format(self.Tdur))
logging.debug('aR={0}, cosi={1}, RR={2}'.format(aR, cosi, RR))
logging.debug('arcsin arg={}'.format(
1 / aR * (((1 + RR)**2 - (aR * cosi)**2) / (1 - cosi**2))**(0.5)))
logging.debug('inside sqrt in arcsin arg={}'.format(
(((1 + RR)**2 - (aR * cosi)**2) / (1 - cosi**2))))
logging.debug('best-fit impact parameter={:.2f}'.format(
self.rowefit.ix['BB1', 'val']))
lc['t'] += (2450000 + 0.5)
lc['f'] += 1 - self.rowefit.ix['ZPT', 'val']
if self.has_ttvs:
tts['tc'] += 2504900
ts = pd.Series()
fs = pd.Series()
dfs = pd.Series()
if self.has_ttvs:
for t0 in tts['tc']:
t = lc['t'] - t0
ok = np.absolute(t) < 2 * self.Tdur
ts = ts.append(t[ok])
fs = fs.append(lc['f'][ok])
dfs = dfs.append(lc['df'][ok])
else:
center = self.epoch % self.P
t = np.mod(lc['t'] - center + self.P / 2, self.P) - self.P / 2
ok = np.absolute(t) < 2 * self.Tdur
ts = t[ok]
fs = lc['f'][ok]
dfs = lc['df'][ok]
logging.debug('{0}: has_ttvs is {1}'.format(koi, self.has_ttvs))
logging.debug('{} light curve points used'.format(ok.sum()))
if maxslope is None:
#set maxslope using duration
maxslope = max(Tdur * 24 / 0.5 * 2,
30) #hardcoded in transitFPP as default=30
p0 = [Tdur, RR**2, 3, 0]
self.p0 = p0
logging.debug('initial trapezoid parameters guess: {}'.format(p0))
TransitSignal.__init__(self,
np.array(ts),
np.array(fs),
np.array(dfs),
p0=p0,
name=koiname(koi),
P=P,
maxslope=maxslope)
if mcmc:
self.MCMC(refit=refit_mcmc)
if self.hasMCMC and not self.fit_converged:
logging.warning(
'Trapezoidal MCMC fit did not converge for {}.'.format(
self.name))
def __init__(self, koi, recalc=False,
use_JRowe=True, trsig_kws=None,
tag=None, starmodel_mcmc_kws=None,
**kwargs):
koi = koiname(koi)
#if saved popset exists, load
folder = os.path.join(KOI_FPPDIR,koi)
if tag is not None:
folder += '_{}'.format(tag)
if not os.path.exists(folder):
os.makedirs(folder)
if trsig_kws is None:
trsig_kws = {}
#first check if pickled signal is there to be loaded
trsigfile = os.path.join(folder,'trsig.pkl')
if os.path.exists(trsigfile):
trsig = pickle.load(open(trsigfile,'rb'))
else:
if use_JRowe:
trsig = JRowe_KeplerTransitSignal(koi, **trsig_kws)
else:
trsig = KeplerTransitSignal(koi, **trsig_kws)
popsetfile = os.path.join(folder,'popset.h5')
if os.path.exists(popsetfile) and not recalc:
popset = PopulationSet(popsetfile, **kwargs)
else:
koinum = koiname(koi, koinum=True)
kepid = ku.DATA.ix[koi,'kepid']
if 'mass' not in kwargs:
kwargs['mass'] = koi_propdist(koi, 'mass')
if 'radius' not in kwargs:
kwargs['radius'] = koi_propdist(koi, 'radius')
if 'feh' not in kwargs:
kwargs['feh'] = koi_propdist(koi, 'feh')
if 'age' not in kwargs:
try:
kwargs['age'] = koi_propdist(koi, 'age')
except:
kwargs['age'] = (9.7,0.1) #default age
if 'Teff' not in kwargs:
kwargs['Teff'] = kicu.DATA.ix[kepid,'teff']
if 'logg' not in kwargs:
kwargs['logg'] = kicu.DATA.ix[kepid,'logg']
if 'rprs' not in kwargs:
if use_JRowe:
kwargs['rprs'] = trsig.rowefit.ix['RD1','val']
else:
kwargs['rprs'] = ku.DATA.ix[koi,'koi_ror']
#if stellar properties are determined spectroscopically,
# fit stellar model
if 'starmodel' not in kwargs:
if re.match('SPE', kicu.DATA.ix[kepid, 'teff_prov']):
logging.info('Spectroscopically determined stellar properties.')
#first, see if there already is a starmodel to load
#fit star model
Teff = kicu.DATA.ix[kepid, 'teff']
e_Teff = kicu.DATA.ix[kepid, 'teff_err1']
logg = kicu.DATA.ix[kepid, 'logg']
e_logg = kicu.DATA.ix[kepid, 'logg_err1']
feh = kicu.DATA.ix[kepid, 'feh']
e_feh = kicu.DATA.ix[kepid, 'feh_err1']
logging.info('fitting StarModel (Teff=({},{}), logg=({},{}), feh=({},{}))...'.format(Teff, e_Teff, logg, e_logg, feh, e_feh))
dar = Dartmouth_Isochrone()
starmodel = StarModel(dar, Teff=(Teff, e_Teff),
logg=(logg, e_logg),
feh=(feh, e_feh))
if starmodel_mcmc_kws is None:
starmodel_mcmc_kws = {}
starmodel.fit(**starmodel_mcmc_kws)
logging.info('Done.')
kwargs['starmodel'] = starmodel
if 'mags' not in kwargs:
kwargs['mags'] = ku.KICmags(koi)
if 'ra' not in kwargs:
kwargs['ra'], kwargs['dec'] = ku.radec(koi)
if 'period' not in kwargs:
kwargs['period'] = ku.DATA.ix[koi,'koi_period']
if 'pl_kws' not in kwargs:
kwargs['pl_kws'] = {}
if 'fp_specific' not in kwargs['pl_kws']:
rp = kwargs['radius'].mu * kwargs['rprs'] * RSUN/REARTH
kwargs['pl_kws']['fp_specific'] = fp_fressin(rp)
#trilegal_filename = os.path.join(folder,'starfield.h5')
trilegal_filename = kepler_starfield_file(koi)
popset = PopulationSet(trilegal_filename=trilegal_filename,
**kwargs)
#popset.save_hdf('{}/popset.h5'.format(folder), overwrite=True)
lhoodcachefile = os.path.join(folder,'lhoodcache.dat')
self.koi = koi
FPPCalculation.__init__(self, trsig, popset,
folder=folder)
self.save()
self.apply_default_constraints()
