def _bulk_detrend(self, detrend_kwargs, to_plot=False):
"""Smooth the rapid variations in the lightcurve and remove bulk trends.
inputs
------
alpha: float
"bass enhancement" for supersmoother.
"""
if detrend_kwargs is None:
detrend_kwargs = dict()
detrend_kwargs["kind"] = detrend_kwargs.get("kind", "supersmoother")
detrend_kwargs["phaser"] = detrend_kwargs.get("phaser", None)
logging.debug("Removing bulk trend...")
det_out = detrend.simple_detrend(self.time, self.flux, self.unc_flux,
to_plot=to_plot, **detrend_kwargs)
self.det_flux, self.det_unc, self.bulk_trend = det_out
logging.debug("len detrended t %d f %d u %d", len(self.time),
len(self.det_flux),len(self.det_unc))
if to_plot==True:
fig = plt.gcf()
fig.suptitle("{}; {} ({})".format(self.name,detrend_kwargs["kind"],
detrend_kwargs["phaser"]),fontsize="x-large")
plt.savefig("{0}plot_outputs/{1}_detrend.png".format(base_path,
self.name))
def smooth_and_clip(time, flux, unc_flux, clip_at=3, to_plot=False):
"""Smooth the lightcurve, then clip based on residuals."""
if to_plot:
plt.figure(figsize=(8,4))
ax = plt.subplot(111)
ax.plot(time,flux,'k.',label="orig")
# Simple sigma clipping first to get rid of really big outliers
ct, cf, cu, to_keep = sigma_clip(time, flux, unc_flux, clip_at=clip_at)
logging.debug("c len t %d f %d u %d tk %d", len(ct), len(cf),
len(cu), len(to_keep))
if to_plot: ax.plot(ct, cf, '.',label="-1")
# Smooth with supersmoother without much bass enhancement
for i in range(3):
det_out = detrend.simple_detrend(ct, cf, cu, phaser=0)
detrended_flux, detrended_unc, bulk_trend = det_out
# Take the difference, and find the standard deviation of the residuals
# logging.debug("flux, bulk trend, diff")
# logging.debug(cf[:5])
# logging.debug(bulk_trend[:5])
f_diff = cf - bulk_trend
# logging.debug(f_diff[:5])
diff_std = np.zeros(len(f_diff))
diff_std[ct<=2102] = np.std(f_diff[ct<=2102])
diff_std[ct>2102] = np.std(f_diff[ct>2102])
# logging.debug("std %f %f",diff_std[0], diff_std[-1])
if to_plot:
ax.plot(ct, bulk_trend)
logging.debug("%d len tk %d diff %d", i, len(to_keep), len(f_diff))
# Clip outliers based on residuals this time
to_keep = to_keep[abs(f_diff)<=(diff_std*clip_at)]
ct = time[to_keep]
cf = flux[to_keep]
cu = unc_flux[to_keep]
if to_plot:
ax.plot(ct, cf, '.',label=str(i))
if to_plot:
ax.legend()
clip_time = time[to_keep]
clip_flux = flux[to_keep]
clip_unc_flux = unc_flux[to_keep]
return clip_time, clip_flux, clip_unc_flux, to_keep
def smooth_and_clip(time, flux, unc_flux, clip_at=3, to_plot=False):
"""Smooth the lightcurve, then clip based on residuals."""
if to_plot:
plt.figure(figsize=(8, 4))
ax = plt.subplot(111)
ax.plot(time, flux, 'k.', label="orig")
# Simple sigma clipping first to get rid of really big outliers
ct, cf, cu, to_keep = sigma_clip(time, flux, unc_flux, clip_at=clip_at)
logging.debug("c len t %d f %d u %d tk %d", len(ct), len(cf), len(cu),
len(to_keep))
if to_plot: ax.plot(ct, cf, '.', label="-1")
# Smooth with supersmoother without much bass enhancement
for i in range(3):
det_out = detrend.simple_detrend(ct, cf, cu, phaser=0)
detrended_flux, detrended_unc, bulk_trend = det_out
# Take the difference, and find the standard deviation of the residuals
# logging.debug("flux, bulk trend, diff")
# logging.debug(cf[:5])
# logging.debug(bulk_trend[:5])
f_diff = cf - bulk_trend
# logging.debug(f_diff[:5])
diff_std = np.zeros(len(f_diff))
diff_std[ct <= 2102] = np.std(f_diff[ct <= 2102])
diff_std[ct > 2102] = np.std(f_diff[ct > 2102])
# logging.debug("std %f %f",diff_std[0], diff_std[-1])
if to_plot:
ax.plot(ct, bulk_trend)
logging.debug("%d len tk %d diff %d", i, len(to_keep), len(f_diff))
# Clip outliers based on residuals this time
to_keep = to_keep[abs(f_diff) <= (diff_std * clip_at)]
ct = time[to_keep]
cf = flux[to_keep]
cu = unc_flux[to_keep]
if to_plot:
ax.plot(ct, cf, '.', label=str(i))
if to_plot:
ax.legend()
clip_time = time[to_keep]
clip_flux = flux[to_keep]
clip_unc_flux = unc_flux[to_keep]
return clip_time, clip_flux, clip_unc_flux, to_keep
