def inject_and_tls_search_by_tic(tic_id, sigma_multiplier=3, **kwargs):
'''
Summary:
-------
- wrapper around inject_and_tls_search()
- retrieves the SPOC PDC-SAP lightcurve
- retrieves all TIC catalog information from MAST
- injects a planet signal via ellc, for a given period and radius (at random epoch)
- runs TLS on these injected data and infos
Inputs:
-------
tic_id : str or int
TIC ID
Optional Inputs:
----------------
sigma_multiplier : float
set TLS' uniform bounds for the host's radius and mass at sigma_multiplier times the error bars from TICv8
default is 3
**kwargs : keyword arguments
any other keyword arguments, see inject_and_tls_search()
Returns:
-------
a list of all TLS results will get saved to a log file
'''
#::: format inputs
tic_id = str(int(tic_id))
#::: load data
time, flux, flux_err = tessio.get(tic_id,
pipeline='spoc',
PDC=True,
unpack=True)
#::: load TIC info
ldc, R_host, R_host_lerr, R_host_uerr, M_host, M_host_lerr, M_host_uerr = catalog_info(
TIC_ID=int(tic_id))
print('\nTICv8 info:')
print('Quadratic limb darkening u_0, u_1', ldc[0], ldc[1])
print('Stellar radius', R_host, '+', R_host_lerr, '-', R_host_uerr)
print('Stellar mass', M_host, '+', M_host_lerr, '-', M_host_uerr)
#::: run
inject_and_tls_search(time,
flux,
flux_err,
R_host=R_host,
R_host_min=R_host - sigma_multiplier * R_host_lerr,
R_host_max=R_host + sigma_multiplier * R_host_uerr,
M_host=M_host,
M_host_min=M_host - sigma_multiplier * M_host_lerr,
M_host_max=M_host + sigma_multiplier * M_host_uerr,
ldc=ldc,
**kwargs)
def tls_search_by_tic(tic_id,
tls_kwargs=None,
SNR_threshold=5.,
known_transits=None,
options=None):
'''
Summary:
-------
wrapper around tls_search()
retrieves the SPOC PDC-SAP lightcurve
retrieves all TIC catalog information from MAST
calls tls_search()
Inputs:
-------
tic_id : str
TIC ID
Optional Inputs:
----------------
see tls_search()
Returns:
-------
list of all TLS results
'''
#::: handle inputs
if options is None: options = {}
if 'show_plot' not in options: options['show_plot'] = False
if 'save_plot' not in options: options['save_plot'] = False
if 'outdir' not in options: options['outdir'] = ''
#::: format inputs
tic_id = str(int(tic_id))
#::: load data and inject transit
time, flux, flux_err = tessio.get(tic_id,
pipeline='spoc',
PDC=True,
unpack=True)
#::: load TIC info / tls kwargs
tls_kwargs = get_tls_kwargs_by_tic(tic_id, tls_kwargs=tls_kwargs)
return tls_search(time,
flux,
flux_err,
tls_kwargs=tls_kwargs,
SNR_threshold=SNR_threshold,
known_transits=known_transits,
options=options)
def inject_and_tls_search_by_tic(tic_id,
periods,
rplanets,
logfname,
SNR_threshold=5.,
known_transits=None,
show_plot=False,
save_plot=False):
'''
Inputs:
-------
tic_id : str or int
TIC ID
periods : float or array of float
a period or list of periods for injections
rplanets : float or array of float
a planet radius or list of planet radii for injections
logfname : str
file path and name for the log file
SNR_threshold : float
the SNR threshold at which to stop the TLS search
known_transits : None or dict
if dict and one transit is already known:
known_transits = {'period':[1.3], 'duration':[2.1], 'epoch':[245800.0]}
if dict and multiple transits are already known:
known_transits = {'period':[1.3, 21.0], 'duration':[2.1, 4.1], 'epoch':[245800.0, 245801.0]}
'period' is the period of the transit
'duration' must be the total duration, i.e. from first ingress point to last egrees point, in days
'epoch' is the epoch of the transit
show_plot : bool
show a plot in the terminal or not
save_plot : bool
save a plot
Summary:
-------
- retrieves the SPOC PDC-SAP lightcurve
- retrieves all TIC catalog information from MAST
- injects a planet signal via ellc, for a given period and radius (at random epoch)
- runs TLS on these injected data and infos
Returns:
-------
- a list of all TLS results will get saved to a log file
'''
#::: format inputs
tic_id = str(int(tic_id))
periods = np.atleast_1d(periods)
rplanets = np.atleast_1d(rplanets)
#::: load data
dic = tessio.get(tic_id)
time, flux, flux_err = dic['time'], dic['flux'], dic['flux_err']
#::: load TIC info
ab, R_star, R_star_lerr, R_star_uerr, M_star, M_star_lerr, M_star_uerr = catalog_info(
TIC_ID=int(tic_id))
print('TICv8 info:')
print('Quadratic limb darkening a, b', ab[0], ab[1])
print('Stellar radius', R_star, '+', R_star_lerr, '-', R_star_uerr)
print('Stellar mass', M_star, '+', M_star_lerr, '-', M_star_uerr)
#::: run
inject_and_tls_search(time,
flux,
flux_err,
periods,
rplanets,
logfname,
SNR_threshold=SNR_threshold,
known_transits=known_transits,
R_star=R_star,
R_star_min=R_star - R_star_lerr,
R_star_max=R_star + R_star_uerr,
M_star=M_star,
M_star_min=M_star - M_star_lerr,
M_star_max=M_star + M_star_uerr,
show_plot=show_plot,
save_plot=save_plot)
def inject_and_tls_search_by_tic(tic_id,
period_grid, r_companion_earth_grid,
SNR_threshold=5.,
known_transits=None,
tls_kwargs=None,
options=None):
'''
Inputs:
-------
tic_id : str or int
TIC ID
periods : float or array of float
a period or list of periods for injections
rplanets : float or array of float
a planet radius or list of planet radii for injections
logfname : str
file path and name for the log file
SNR_threshold : float
the SNR threshold at which to stop the TLS search
known_transits : None or dict
if dict and one transit is already known:
known_transits = {'period':[1.3], 'duration':[2.1], 'epoch':[245800.0]}
if dict and multiple transits are already known:
known_transits = {'period':[1.3, 21.0], 'duration':[2.1, 4.1], 'epoch':[245800.0, 245801.0]}
'period' is the period of the transit
'duration' must be the total duration, i.e. from first ingress point to last egrees point, in days
'epoch' is the epoch of the transit
show_plot : bool
show a plot in the terminal or not
save_plot : bool
save a plot
Summary:
-------
- retrieves the SPOC PDC-SAP lightcurve
- retrieves all TIC catalog information from MAST
- injects a planet signal via ellc, for a given period and radius (at random epoch)
- runs TLS on these injected data and infos
Returns:
-------
- a list of all TLS results will get saved to a log file
'''
#::: format inputs
tic_id = str(int(tic_id))
period_grid = np.atleast_1d(period_grid)
r_companion_earth_grid = np.atleast_1d(r_companion_earth_grid)
#::: load data
dic = tessio.get(tic_id)
time, flux, flux_err = dic['time'], dic['flux'], dic['flux_err']
#::: load tls kwargs
tls_kwargs = get_tls_kwargs_by_tic(tic_id, tls_kwargs=tls_kwargs)
#::: run
inject_and_tls_search(time, flux, flux_err,
period_grid, r_companion_earth_grid,
SNR_threshold=SNR_threshold,
known_transits=known_transits,
tls_kwargs=tls_kwargs,
options=options)
import seaborn as sns
sns.set(context='paper',
style='ticks',
palette='deep',
font='sans-serif',
font_scale=1.5,
color_codes=True)
sns.set_style({"xtick.direction": "in", "ytick.direction": "in"})
sns.set_context(rc={'lines.markeredgewidth': 1})
#::: 0) tic_id
tic_id = '390651552'
#::: 1) load data
time, flux, flux_err = tessio.get(tic_id, unpack=True)
tessplot(time, flux, clip=False)
tessplot(time, flux)
#::: 2) prepare data
flux = sigma_clip(time, flux, high=3, low=20)
flux = slide_clip(time, flux, high=3, low=20)
flux = mask_regions(time,
flux,
regions=[(2458865.5, 2458866), (2458990, 2458990.5)])
tessplot(time, flux)
#::: 3) estimate variability period
period = estimate_period(time, flux, flux_err)[0]
#::: 4) detrend data
def inject_and_tls_search_by_tic(tic_id,
period_grid,
r_companion_earth_grid,
known_transits=None,
tls_kwargs=None,
wotan_kwargs=None,
options=None):
'''
Inputs:
-------
tic_id : str or int
TIC ID
periods : float or array of float
a period or list of periods for injections
rplanets : float or array of float
a planet radius or list of planet radii for injections
logfname : str
file path and name for the log file
SNR_threshold : float
the SNR threshold at which to stop the TLS search
known_transits : None or dict
if dict and one transit is already known:
known_transits = {'period':[1.3], 'duration':[2.1], 'epoch':[245800.0]}
if dict and multiple transits are already known:
known_transits = {'period':[1.3, 21.0], 'duration':[2.1, 4.1], 'epoch':[245800.0, 245801.0]}
'period' is the period of the transit
'duration' must be the total duration, i.e. from first ingress point to last egrees point, in days
'epoch' is the epoch of the transit
show_plot : bool
show a plot in the terminal or not
save_plot : bool
save a plot
Summary:
-------
- retrieves the SPOC PDC-SAP lightcurve
- retrieves all TIC catalog information from MAST
- injects a planet signal via ellc, for a given period and radius (at random epoch)
- runs TLS on these injected data and infos
Returns:
-------
- a list of all TLS results will get saved to a log file
'''
#::: format inputs
tic_id = str(int(tic_id))
period_grid = np.atleast_1d(period_grid)
r_companion_earth_grid = np.atleast_1d(r_companion_earth_grid)
#::: load data
time, flux, flux_err = tessio.get(tic_id, unpack=True)
if (time is None) or (
len(time) < 5000
): #abort if no reasonable data was found (at least 5000 exposures, aka 7 days of data)
print('Skipped. No data was found for TIC ' + tic_id + '.')
return None
#::: load tls kwargs
tls_kwargs = get_tls_kwargs_by_tic(tic_id, tls_kwargs=tls_kwargs)
if any(
np.isnan([
tls_kwargs['u'][0], tls_kwargs['u'][1], tls_kwargs['R_star'],
tls_kwargs['M_star']
])): #abort if TICv8 doesn't hold the stellar info
print('Skipped. No TICv8 information was found for TIC ' + tic_id +
'.')
return None
#::: run
inject_and_tls_search(time,
flux,
flux_err,
period_grid,
r_companion_earth_grid,
known_transits=known_transits,
tls_kwargs=tls_kwargs,
wotan_kwargs=wotan_kwargs,
options=options)
def tls_search_by_tic(tic_id,
time=None,
flux=None,
flux_err=None,
SNR_threshold=5.,
known_transits=None,
show_plot=False,
save_plot=False,
outdir=''):
'''
Inputs:
-------
tic_id : str
TIC ID
Optional Inputs:
----------------
time : array of flaot
time stamps of observations (to overwrite the SPOC PDC SAP lightcurve)
flux : array of flaot
normalized flux (to overwrite the SPOC PDC SAP lightcurve)
flux_err : array of flaot
error of normalized flux (to overwrite the SPOC PDC SAP lightcurve)
SNR_threshold : float
the SNR threshold at which to stop the TLS search
known_transits : None or dict
if dict and one transit is already known:
known_transits = {'period':[1.3], 'duration':[2.1], 'epoch':[245800.0]}
if dict and multiple transits are already known:
known_transits = {'period':[1.3, 21.0], 'duration':[2.1, 4.1], 'epoch':[245800.0, 245801.0]}
'period' is the period of the transit
'duration' must be the total duration, i.e. from first ingress point to last egrees point, in days
'epoch' is the epoch of the transit
Summary:
-------
- retrieves the SPOC PDC-SAP lightcurve
- retrieves all TIC catalog information from MAST
- runs TLS on these data and infos
Returns:
-------
- list of all TLS results
'''
#::: format inputs
tic_id = str(int(tic_id))
#::: load data and inject transit
dic = tessio.get(tic_id, pipeline='spoc', PDC=True)
if time is None: time = dic['time']
if flux is None: flux = dic['flux']
if flux_err is None: flux_err = dic['flux_err']
#::: load TIC info
ab, R_star, R_star_lerr, R_star_uerr, M_star, M_star_lerr, M_star_uerr = catalog_info(
TIC_ID=int(tic_id))
print('TICv8 info:')
print('Quadratic limb darkening a, b', ab[0], ab[1])
print('Stellar radius', R_star, '+', R_star_lerr, '-', R_star_uerr)
print('Stellar mass', M_star, '+', M_star_lerr, '-', M_star_uerr)
return tls_search(time,
flux,
flux_err,
R_star=R_star,
R_star_min=R_star - R_star_lerr,
R_star_max=R_star + R_star_uerr,
M_star=M_star,
M_star_min=M_star - M_star_lerr,
M_star_max=M_star + M_star_uerr,
known_transits=known_transits,
show_plot=show_plot,
save_plot=save_plot,
outdir=outdir)