def multi_search(self, to_plot=False):
"""Search a lightcurve for a secondary signal."""
# Start with the corrected lightcurve and its associated period
# Phase on that period and remove it
white_out = detrend.pre_whiten(self.time, self.corrected_flux,
self.corrected_unc, self.corr_prot,
which="phased")
detrended_flux = self.corrected_flux / white_out[2]
self.corr_trend = white_out[2]
self.sec_flux = detrended_flux
self.sec_unc = self.corrected_unc
# Run lomb-scargle again and re-measure the period
fit_out = self._run_fit([self.time, self.sec_flux, self.sec_unc])
self.sec_prot = fit_out[0]
self.sec_power = fit_out[1]
self.sec_periods = fit_out[2]
self.sec_pgram = fit_out[3]
self.sec_sigmas = fit_out[5]
eval_out = evaluate.test_pgram(self.sec_periods, self.sec_pgram,
self.power_threshold)
plot_aliases = [None, eval_out[2]]
white_out2 = detrend.pre_whiten(self.time, self.sec_flux,
self.sec_unc, self.sec_prot,
which="phased")
self.sec_trend = white_out2[2]
# Plot!
if to_plot:
# Plot them up
lcs = [[self.time, self.corrected_flux, self.corrected_unc],
[self.time, self.sec_flux, self.sec_unc]]
pgrams = [[self.corr_periods, self.corr_pgram],
[self.sec_periods, self.sec_pgram]]
best_periods = [self.corr_prot, self.sec_prot]
data_labels = ["Corrected", "Fund. Prot="
"{0:.2f}d Removed".format(self.corr_prot)]
sigmas = [self.corr_sigmas, self.sec_sigmas]
rd_fig, rd_axes = plot.compare_multiple(lcs, pgrams, best_periods,
sigmas,
aliases=plot_aliases,
data_labels=data_labels,
phase_by=self.sec_prot)
rd_fig.suptitle(self.name, fontsize="large", y=0.99)
rd_fig.delaxes(rd_axes[3])
rd_axes[0].plot(self.time, white_out[2], 'b-', lw=2)
plt.savefig("{0}plot_outputs/{1}_second_period.png".format(
base_path,self.name))
def choose_initial(self, to_plot=False):
"""Search raw and detrended LCs for periods, and decide whether there's
a period there.
"""
# Run a fit on the raw lc
r_out = self._run_fit("raw")
raw_fp, raw_power, raw_prots, raw_pgram, raw_alias, raw_sigma = r_out
logging.debug("Ran raw fit")
# Run a fit on the detrended lc
d_out = self._run_fit("detrended")
det_fp, det_power, det_prots, det_pgram, det_alias, det_sigma = d_out
logging.debug("Ran detrended fit")
# Only consider peaks less than ~half the length of the lightcurve
# max_peak_loc = 0.75 * (self.time[-1] - self.time[0])
max_peak_loc = 40
logging.info("Max Prot = %f", max_peak_loc)
raw_loc2 = np.argmax(raw_pgram[raw_prots<max_peak_loc])
raw_power2 = raw_pgram[raw_prots<max_peak_loc][raw_loc2]
raw_prot2 = raw_prots[raw_prots<max_peak_loc][raw_loc2]
logging.info("raw %d FP %f Power %f", raw_loc2, raw_prot2, raw_power2)
det_loc2 = np.argmax(det_pgram[det_prots<max_peak_loc])
det_power2 = det_pgram[det_prots<max_peak_loc][det_loc2]
det_prot2 = det_prots[det_prots<max_peak_loc][det_loc2]
logging.info("det %d FP %f Power %f", det_loc2, det_prot2, det_power2)
# Compare them
lc_to_use = self._pick_lc(raw_power2, det_power2)
if lc_to_use<=1:
logging.info("Using raw lightcurve")
self.init_prot , self.init_power = raw_prot2, raw_power2
self.init_periods_to_test, self.init_pgram = raw_prots, raw_pgram
self.use_flux = self.flux / self.med
self.use_unc = self.unc_flux / self.med
self.init_sigmas = raw_sigma
self.use = "raw"
data_labels = ["Raw (Selected)", "Detrended"]
elif lc_to_use==2:
logging.info("Using detrended lightcurve")
self.init_prot , self.init_power = det_prot2, det_power2
self.init_periods_to_test, self.init_pgram = det_prots, det_pgram
self.use_flux = self.det_flux
self.use_unc = self.unc_flux
self.init_sigmas = det_sigma
self.use = "det"
data_labels = ["Raw", "Detrended (Selected)"]
logging.info("Initial Prot %f Power %f", self.init_prot,
self.init_power)
# get power at harmonics
self.init_harmonics = self._harmonics(self.init_prot,
self.init_periods_to_test,
self.init_pgram)
# Get aliases for selected period
eval_out = evaluate.test_pgram(self.init_periods_to_test,
self.init_pgram, self.power_threshold)
# Get phase-folded, smoothed trend
white_out2 = detrend.pre_whiten(self.time, self.use_flux,
self.use_unc, self.init_prot,
which="phased")
self.init_trend = white_out2[2]
if eval_out[-1]==False:
logging.warning("Selected lightcurve is not clean")
else:
logging.debug("Selected lightcurve is clean")
plot_aliases = [None, eval_out[2]]
if to_plot:
# Plot them up
lcs = [[self.time, self.flux/self.med, abs(self.unc_flux/self.med)],
[self.time, self.det_flux, self.det_unc]]
pgrams = [[raw_prots, raw_pgram], [det_prots, det_pgram]]
best_periods = [raw_prot2, det_prot2]
sigmas = [raw_sigma, det_sigma]
logging.debug(sigmas)
rd_fig, rd_axes = plot.compare_multiple(lcs, pgrams, best_periods,
sigmas,
aliases=plot_aliases,
data_labels=data_labels,
phase_by=self.init_prot)
rd_fig.suptitle(self.name, fontsize="large", y=0.99)
rd_fig.delaxes(rd_axes[3])
plt.savefig("{0}plot_outputs/{1}_raw_detrend.png".format(base_path,
self.name))
plt.close("all")
def search_and_detrend(time,
flux,
unc_flux,
prot_lims=None,
to_plot=False,
**detrend_kwargs):
"""Test for a period and then pre-whiten with it.
Inputs
------
time, flux, unc_flux: array_like
kind: string, optional
type of smoothing to use. Defaults to "supersmoother."
Other types "boxcar", "linear"
which: string, optional
whether to smooth the "phased" lightcurve (default) or the "full"
lightcurve.
phaser: Float, optional (default=None)
if kind="boxcar", phaser is the Half-width of the smoothing window.
if kind="supersmoother", phaser is alpha (the "bass enhancement").
pgram_threshold: float
prot_lims: list-like, length=2
minimum and maximum rotation periods to search for lomb-scargle
num_prot: integer
How many rotation periods to search
Outputs
-------
fund_period, fund_power, periods_to_test, periodogram
white_flux, white_unc, smoothed_flux
"""
# Search for periodogram
ls_out = run_ls(time, flux, unc_flux, 0.5, prot_lims=prot_lims)
fund_period, fund_power, periods_to_test, periodogram = ls_out[:4]
# Whiten on that period
white_out = detrend.pre_whiten(time,
flux,
unc_flux,
fund_period,
which="phased",
**detrend_kwargs)
white_flux, white_unc, smoothed_flux = white_out
detrended_flux = flux / smoothed_flux
detrended_unc = unc_flux
if to_plot == True:
fig, ax_list = plot.plot_one([time, flux, unc_flux],
[periods_to_test, periodogram],
fund_period,
power_threshold=0,
data_label="Input")
ax_list[0].plot(time, smoothed_flux, 'b.')
ax_list[3].plot(time, white_flux, 'r.')
ax_list[3].set_ylabel("Whitened Flux")
ax_list[3].set_xlabel("Time (D)")
plt.tight_layout()
# Return the detrended flux
return detrended_flux, detrended_unc, fund_period, fund_power
def search_and_detrend(time, flux, unc_flux, prot_lims=None,
to_plot=False, **detrend_kwargs):
"""Test for a period and then pre-whiten with it.
Inputs
------
time, flux, unc_flux: array_like
kind: string, optional
type of smoothing to use. Defaults to "supersmoother."
Other types "boxcar", "linear"
which: string, optional
whether to smooth the "phased" lightcurve (default) or the "full"
lightcurve.
phaser: Float, optional (default=None)
if kind="boxcar", phaser is the Half-width of the smoothing window.
if kind="supersmoother", phaser is alpha (the "bass enhancement").
pgram_threshold: float
prot_lims: list-like, length=2
minimum and maximum rotation periods to search for lomb-scargle
num_prot: integer
How many rotation periods to search
Outputs
-------
fund_period, fund_power, periods_to_test, periodogram
white_flux, white_unc, smoothed_flux
"""
# Search for periodogram
ls_out = run_ls(time, flux, unc_flux, 0.5,
prot_lims=prot_lims)
fund_period, fund_power, periods_to_test, periodogram = ls_out[:4]
# Whiten on that period
white_out = detrend.pre_whiten(time, flux, unc_flux, fund_period,
which="phased", **detrend_kwargs)
white_flux, white_unc, smoothed_flux = white_out
detrended_flux = flux / smoothed_flux
detrended_unc = unc_flux
if to_plot==True:
fig, ax_list = plot.plot_one([time, flux, unc_flux],
[periods_to_test, periodogram],
fund_period,
power_threshold=0, data_label="Input")
ax_list[0].plot(time, smoothed_flux, 'b.')
ax_list[3].plot(time, white_flux, 'r.')
ax_list[3].set_ylabel("Whitened Flux")
ax_list[3].set_xlabel("Time (D)")
plt.tight_layout()
# Return the detrended flux
return detrended_flux, detrended_unc, fund_period, fund_power
