def bls_vetsheet(lc, results=0, show=True, save=False, savename='vetsheet.png'):
"""
Function to plot a vetting sheet after running the
transit_tools.signal_search function. Output is formatted to fit onto a
standard 8"x11" sheet of paper.
Parameters
----------
lc : `transit_tools.lightcurve` object
Input transit_tools `transit_tools.lightcurve` object that has had a
BLS signal search performed on it.
results : int
Index of results attribute array of provided 'lightcurve' object.
Indicates which set of results to plot if signal_search produced more
than one set of output results. Can be set to -1 to display the most
recent signal run that did not meet the significance threshold.
show : bool or str
Flag to determine whether plots will be displayed or not. Must be set to
False or 'both' for output matplotlib object to be expected.
save : bool
Flag to determine whether the plots will be saved as a PNG.
savename : str or None
File name for plots to be saved as if save is set to True.
Returns
-------
plots : matplotlib object
Output matplotlib plot object. Optional if show is set to False or
'both'.
"""
if not hasattr(lc, 'results') or len(lc.results) == 0:
raise ValueError('lightcurve object has no results.')
elif len(lc.results) > 0:
res = lc.results[results]
bls = lc.blsobjs[results] #rerun on cleanlc[results]?
model = bls.get_transit_model(period=res['period'],
transit_time=res['t0'],
duration=res['duration'])
if results == 0 or len(lc.results) == 0:
time = lc.time
flux = lc.flux
flux_err = lc.flux_err
else:
time = lc.cleanlc[results].time
flux = lc.cleanlc[results].flux
flux_err = lc.cleanlc[results].flux_err
lc_tmp = LightCurve(time, flux, flux_err)
#setting up figure
fig = plt.figure(figsize=(8, 9.2))
gs = fig.add_gridspec(5, 2)
#phase-folded light curve with transit model
ax = fig.add_subplot(gs[0, 1])
fold = lc_tmp.fold(res['period'], t0=res['t0'])
binfold = fold.bin(20)
ax.scatter(fold.time, fold.flux, color='blue', s=0.2,
alpha=0.5, zorder=2)
model.fold(res['period'], t0=res['t0']).plot(ax=ax, color='r', lw=2,
alpha=0.7)
ax.scatter(binfold.time, binfold.flux, c='k', s=2.)
ax.set_xlim(-0.05, 0.05)
ax.set_xlabel('Phase')
ax.set_ylabel('Relative Flux')
plt.tight_layout()
if show:
plt.show()
if save:
plt.savefig(savename)
def tls_vetsheet(lc, results=0, show=True, save=False, savename='vetsheet.png'):
#!!Add functionality to plot without having to run search?!!
#!!Add vertical lines for expected transit times on unprocessed LC!!
#!!Change x-axis phase values for odd-even plot!!
#!!Make processed and unprocessed x-axes line up, especially when LC is
# masked. Maybe include grayed-out parts that were masked!!
"""
Function to plot a vetting sheet after running the
transit_tools.signal_search function. Output is formatted to fit onto a
standard 8"x11" sheet of paper.
Parameters
----------
lc : `transit_tools.lightcurve` object
Input transit_tools `transit_tools.lightcurve` object that has had a
TLS signal search performed on it.
results : int
Index of results attribute array of provided 'lightcurve' object.
Indicates which set of results to plot if signal_search produced more
than one set of output results. Can be set to -1 to display the most
recent signal run that did not meet the significance threshold.
show : bool or str
Flag to determine whether plots will be displayed or not. Must be set to
False or 'both' for output matplotlib object to be expected.
save : bool
Flag to determine whether the plots will be saved as a PNG.
savename : str or None
File name for plots to be saved as if save is set to True.
Returns
-------
plots : matplotlib object
Output matplotlib plot object. Optional if show is set to False or
'both'.
"""
if results == -1 and len(lc.results) > 0:
time = lc.cleanlc[-1].time.value
flux = lc.cleanlc[-1].flux.value
flux_err = lc.cleanlc[-1].flux_err.value
elif len(lc.results) > 0:
res = lc.results[results]
if results == -1:
res = lc.bad_search[0]
if results == 0 or len(lc.results) == 0:
time = lc.time.value
flux = lc.flux.value
flux_err = lc.flux_err.value
else:
time = lc.cleanlc[results-1].time.value
flux = lc.cleanlc[results-1].flux.value
flux_err = lc.cleanlc[results-1].flux_err.value
#setting up figure
fig = plt.figure(figsize=(8, 9.2))
gs = fig.add_gridspec(5, 2)
#phase-folded light curve with transit model
ax = fig.add_subplot(gs[0, 1])
fold = LightCurve(res.folded_phase, res.folded_y).bin(20)
ax.plot(res.model_folded_phase, res.model_folded_model, color='red',
alpha=0.7)
ax.scatter(res.folded_phase, res.folded_y, color='blue', s=0.2, alpha=0.5,
zorder=2)
ax.scatter(fold.time.value, fold.flux.value, s=2., c='k')
ax.set_xlim(0.45, 0.55)
ax.set_xlabel('Phase')
ax.set_ylabel('Relative Flux')
#raw light curve
ax = fig.add_subplot(gs[1, :])
ax.scatter(lc.raw_lc.time.value, lc.raw_lc.flux.value, s=0.2, c='b',
alpha=0.5)
ax.set_xlabel('Time [BTJD]')
ax.set_ylabel('Raw Relative Flux')
ax.set_xlim(lc.raw_lc.time.value.min(), lc.raw_lc.time.value.max())
if hasattr(lc, 'trend'):
plt.plot(lc.trend.time.value, lc.trend.flux.value, c='g')
#processed light curve with transit model
ax = fig.add_subplot(gs[2, :])
transit_time = transit_mask(time, res.period, res.duration, res.T0)
time_notrans = time[~transit_time]
flux_notrans = flux[~transit_time]
flux_err_notrans = flux_err[~transit_time]
ax.scatter(time[transit_time], flux[transit_time], color='red', s=0.2,
zorder=0)
ax.scatter(time[~transit_time], flux[~transit_time], color='blue',
alpha=0.5, s=0.2, zorder=0)
ax.plot(res.model_lightcurve_time, res.model_lightcurve_model, alpha=0.5,
color='red', zorder=1)
ax.set_xlim(time.min(), time.max())
ax.set_ylim(flux.min()-0.001, flux.max()+0.001)
ax.set_xlabel('Time [BTJD]')
ax.set_ylabel('Relative Flux')
#TLS periodogram
ax = fig.add_subplot(gs[3, :])
ax.axvline(res.period, alpha=0.4, lw=3)
ax.set_xlim(np.min(res.periods), np.max(res.periods))
for n in range(2, 10):
ax.axvline(n * res.period, alpha=0.4, lw=1, linestyle='dashed')
ax.axvline(res.period / n, alpha=0.4, lw=1, linestyle='dashed')
ax.set_ylabel('SDE')
ax.set_xlabel('Period [days]')
ax.plot(res.periods, res.power, color='k', lw=0.5)
ax.set_xlim(0, max(res.periods))
#secondary eclipse
ax = fig.add_subplot(gs[4, 0])
ax.plot(res.model_folded_phase-0.5,
np.roll(res.model_folded_model, len(res.model_folded_model)//2),
color='red')
ax.scatter(res.folded_phase-0.5,
np.roll(res.folded_y, len(res.folded_y)//2), color='blue',
s=0.2, alpha=0.5, zorder=2)
ax.set_xlim(-0.05, 0.05)
ax.set_xlabel('Phase')
ax.set_ylabel('Relative Flux')
ax.axvspan(-(res.duration/2/res.period), (res.duration/2/res.period),
alpha=0.3, color='orange', label='Transit Duration')
ax.legend(loc=2, fontsize='x-small')
#Odd-Even comparison
ax = fig.add_subplot(gs[4, 1])
oe = LightCurve(time, flux, flux_err)
oe_odd = oe.fold(2*res.period, res.T0)
oe_even = oe.fold(2*res.period, res.T0+res.period)
ax.scatter(oe_odd.time.value+0.5, oe_odd.flux.value, s=0.2, c='b', label='Odd')
ax.scatter(oe_even.time.value+0.5, oe_even.flux.value, s=0.2, c='r', label='Even')
ax.set_xlim(0.475, 0.525)
ax.set_xlabel('Phase')
ax.set_ylabel('Relative Flux')
ax.set_xticks([0.48, 0.49, 0.50, 0.51, 0.52])
ax.set_xticklabels(['0.46', '0.48', '0.50', '0.52', '0.54'])
ax.legend(loc=2, fontsize='x-small')
#plot summary text
fig.text(0.12, 0.97, s=(str(lc.name) + ' (TIC ' + str(lc.tic) + ')'),
fontweight='bold')
fig.text(0.04, 0.95,
s=(r'P = %.5f +/- %.5f d, $t_{0}$ = %.5f BTJD' %
(res.period, res.period_uncertainty, res.T0)))
fig.text(0.04, 0.93,
s=(r'$T_{dur}$ = %.5f d, %s/%s transits with data' %
(res.duration, res.distinct_transit_count, res.transit_count)))
fig.text(0.04, 0.91,
s=('SDE = %.2f, SNR = %.2f, FAP = %.3e' %
(res.SDE, res.snr, res.FAP)))
fig.text(0.04, 0.89,
s=(r'$R_{P}$/$R_{*}$ = %.4f, $R_{P}$ = %.3f $R_{\bigoplus}$ = %.3f $R_{Jup}$' %
(res.rp_rs,
(lc.star_params_tls['rstar']*res.rp_rs*c.Rsolar_m)/c.Rearth_m,
(lc.star_params_tls['rstar']*res.rp_rs*c.Rsolar_m)/c.Rjup_m)))
fig.text(0.04, 0.87,
s=(r'odd/even mismatch = %.2f $\sigma$, $\delta$ = %.4f' %
(res.odd_even_mismatch, 1-res.depth)))
fig.text(0.04, 0.85,
s=(r'$R_{*}$ = %.2f (+%.2f, -%.2f) $R_{\bigodot}$'
% (lc.star_params_tls['rstar'], lc.star_params_tls['rhigh'],
lc.star_params_tls['rlow'])))
fig.text(0.04, 0.83, s=(r'$M_{*}$ = %.2f (+%.2f, -%.2f) $M_{\bigodot}$' %
(lc.star_params_tls['mstar'],
lc.star_params_tls['mhigh'],
lc.star_params_tls['mlow'])))
if lc.star_params is not None and lc.star_params['Tmag'] is not None:
fig.text(0.04, 0.81, s=('Tmag = %.2f' % (lc.star_params['Tmag'])))
plt.tight_layout()
if show:
plt.show()
if save:
plt.savefig(savename)
