import matplotlib.pyplot as plt
import numpy as np
from thirdway.lightcurve import LightCurve, generate_lc_depth, kepler17_params_db
from thirdway.fitting import peak_finder, summed_gaussians, gaussian
from astropy.utils.console import ProgressBar
# Load light curve from jrad's text archive
light_curve_path = 'data/kepler17_whole.dat'
BJDREF = 2454833.
depth = 0.13413993**2
jd_minus_bjdref, flux, error = np.loadtxt(light_curve_path, unpack=True)
jd = jd_minus_bjdref + BJDREF
kepler17_params = kepler17_params_db()
# construct light curve object from those data
whole_lc = LightCurve(times=jd, fluxes=flux, errors=error)
transits = LightCurve(**whole_lc.mask_out_of_transit(kepler17_params)
).get_transit_light_curves(kepler17_params)
# The short cadence data begin after the 137th transit, so ignore all transits before then:
transits = transits[137:]
plots = True
delta_chi2 = {}#np.zeros(len(transits))
with ProgressBar(len(transits)) as bar:
for i, lc in enumerate(transits):
#lc.plot()
# Remove linear out-of-transit trend from transit
lc.remove_linear_baseline(kepler17_params)
Example ThirdWay experimentations. """ from __future__ import absolute_import, print_function import matplotlib.pyplot as plt import numpy as np from thirdway.lightcurve import LightCurve, generate_lc_depth from thirdway.fitting import run_emcee, summed_gaussians # Load light curve from jrad's text archive light_curve_path = 'data/kepler17_whole.dat' BJDREF = 2454833. jd_minus_bjdref, flux, error = np.loadtxt(light_curve_path, unpack=True) jd = jd_minus_bjdref + BJDREF # construct light curve object from those data whole_lc = LightCurve(times=jd, fluxes=flux, errors=error) transits = LightCurve(**whole_lc.mask_out_of_transit()).get_transit_light_curves() # The short cadence data begin after the 137th transit, so ignore all transits before then: transits = transits[137:] lc = transits[104] # Remove linear out-of-transit trend from transit lc.remove_linear_baseline() depth = 0.13413993**2 residuals = lc.fluxes - generate_lc_depth(lc.times_jd, depth) # Fit the transit model residuals for n_peaks gaussians sampler, samples = run_emcee(lc.times.jd, residuals, lc.errors, n_peaks=4)
