def fit(self, candidate_df, star_df, cpus, allesfit_dir):
candidate_row = candidate_df.iloc[0]
sherlock_star_file = self.object_dir + "/params_star.csv"
star_file = allesfit_dir + "/params_star.csv"
params_file = allesfit_dir + "/params.csv"
settings_file = allesfit_dir + "/settings.csv"
if candidate_row["number"] is None or np.isnan(candidate_row["number"]):
lc_file = "/" + candidate_row["lc"]
else:
lc_file = "/" + str(candidate_row["number"]) + "/lc_" + str(candidate_row["curve"]) + ".csv"
shutil.copyfile(self.object_dir + lc_file, allesfit_dir + "/lc.csv")
if os.path.exists(sherlock_star_file) and os.path.isfile(sherlock_star_file):
shutil.copyfile(sherlock_star_file, star_file)
shutil.copyfile(resources_dir + "/resources/allesfitter/params.csv", params_file)
shutil.copyfile(resources_dir + "/resources/allesfitter/settings.csv", settings_file)
# TODO replace sherlock properties from allesfitter files
# TODO only use params_star when the star mass or radius was not assumed
with open(settings_file, 'r+') as f:
text = f.read()
text = re.sub('\\${sherlock:cores}', str(cpus), text)
text = re.sub('\\${sherlock:name}', str(candidate_row["name"]), text)
detrend_param = "baseline_flux_lc,hybrid_offset"
detrend_param = detrend_param if self.detrend else "#" + detrend_param
text = re.sub('\\${sherlock:detrend}', detrend_param, text)
f.seek(0)
f.write(text)
f.truncate()
with open(params_file, 'r+') as f:
text = f.read()
text = re.sub('\\${sherlock:t0}', str(candidate_row["t0"]), text)
text = re.sub('\\${sherlock:period}', str(candidate_row["period"]), text)
rp_rs = candidate_row["rp_rs"] if candidate_row["rp_rs"] != "-" else 0.1
text = re.sub('\\${sherlock:rp_rs}', str(rp_rs), text)
sum_rp_rs_a = (candidate_row["rp_rs"] + star_df.iloc[0]['R_star']) / candidate_row["a"] * 0.00465047 \
if candidate_row["rp_rs"] != "-" else 0.2
text = re.sub('\\${sherlock:sum_rp_rs_a}', str(sum_rp_rs_a), text)
text = re.sub('\\${sherlock:name}', str(candidate_row["name"]), text)
if os.path.exists(sherlock_star_file) and os.path.isfile(sherlock_star_file):
text = re.sub('\\${sherlock:ld_a}', str(star_df.iloc[0]["ld_a"]) + ",0", text)
text = re.sub('\\${sherlock:ld_b}', str(star_df.iloc[0]["ld_b"]) + ",0", text)
else:
text = re.sub('\\${sherlock:ld_a}', "0.5,1", text)
text = re.sub('\\${sherlock:ld_b}', "0.5,1", text)
f.seek(0)
f.write(text)
f.truncate()
allesfitter.show_initial_guess(allesfit_dir)
if not self.only_initial and not self.mcmc:
allesfitter.ns_fit(allesfit_dir)
allesfitter.ns_output(allesfit_dir)
elif not self.only_initial and self.mcmc:
allesfitter.mcmc_fit(allesfit_dir)
allesfitter.mcmc_output(allesfit_dir)
#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Wed Sep 12 15:57:05 2018 @author: Maximilian N. Günther MIT Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02109, USA Email: [email protected] Web: www.mnguenther.com """ import allesfitter allesfitter.show_initial_guess('allesfit') allesfitter.mcmc_fit('allesfit') allesfitter.mcmc_output('allesfit')
def fit(self, candidate_df, star_df, cpus, allesfit_dir, tolerance,
fit_orbit):
"""
Main method to run the allesfitter fit.
@param candidate_df: the candidates dataframe.
@param star_df: the star dataframe.
@param cpus: the number of cpus to be used.
@param allesfit_dir: the directory for the fit to be run.
@param tolerance: the nested sampling tolerance threshold.
@param fit_orbit: whether to fit eccentricity and arg. of periastron.
"""
logging.info("Preparing fit files")
sherlock_star_file = self.object_dir + "/params_star.csv"
star_file = allesfit_dir + "/params_star.csv"
params_file = allesfit_dir + "/params.csv"
settings_file = allesfit_dir + "/settings.csv"
lc_file = Fitter.select_lc_file(candidate_df)
shutil.copyfile(self.object_dir + lc_file, allesfit_dir + "/lc.csv")
if os.path.exists(sherlock_star_file) and os.path.isfile(
sherlock_star_file):
shutil.copyfile(sherlock_star_file, star_file)
shutil.copyfile(resources_dir + "/resources/allesfitter/settings.csv",
settings_file)
fit_width = Fitter.select_fit_width(candidate_df)
# TODO replace sherlock properties from allesfitter files
# TODO only use params_star when the star mass or radius was not assumed
logging.info("Preparing fit properties")
with open(settings_file, 'r+') as f:
text = f.read()
text = re.sub('\\${sherlock:cores}', str(cpus), text)
text = re.sub('\\${sherlock:fit_width}', str(fit_width), text)
text = re.sub('\\${sherlock:fit_ttvs}', "False", text)
text = re.sub('\\${sherlock:names}',
' '.join(candidate_df["name"].astype('str')), text)
text = re.sub('\\${sherlock:tolerance}', str(tolerance), text)
if self.detrend == 'hybrid_spline':
detrend_param = "baseline_flux_lc,hybrid_spline"
elif self.detrend == 'gp':
detrend_param = 'baseline_flux_lc,sample_GP_Matern32'
else:
detrend_param = ''
text = re.sub('\\${sherlock:detrend}', detrend_param, text)
f.seek(0)
f.write(text)
f.truncate()
with open(params_file, 'w') as f:
f.write(
Fitter.fill_candidates_params(candidate_df, star_df, fit_orbit,
self.detrend))
f.truncate()
logging.info("Running initial guess")
try:
allesfitter.show_initial_guess(allesfit_dir)
except Exception as e:
logging.exception(str(e))
# TODO fix custom_plot for all candidates
# Fitter.custom_plot(candidate_row["name"], candidate_row["period"], fit_width, allesfit_dir, "initial_guess")
if not self.only_initial:
logging.info("Preparing bayesian fit")
if not self.mcmc:
logging.info("Running dynamic nested sampling")
try:
allesfitter.ns_fit(allesfit_dir)
allesfitter.ns_output(allesfit_dir)
except Exception as e:
logging.exception(str(e))
elif self.mcmc:
logging.info("Running MCMC")
try:
allesfitter.mcmc_fit(allesfit_dir)
allesfitter.mcmc_output(allesfit_dir)
except Exception as e:
logging.exception(str(e))
logging.info("Generating custom plots")
"""
import numpy as np
import matplotlib.pyplot as plt
from astropy import constants as c
import allesfitter
from allesfitter.v2.translator import translate
###############################################################################
#::: calculate prior on q (mass ratio) via Stassun+2017
###############################################################################
#Mp = np.random.normal(loc=(11.44*c.M_jup/c.M_sun).value, scale=(1.51*c.M_jup/c.M_sun).value, size=10000)
#Ms = np.random.normal(loc=1.46, scale=0.29, size=10000)
#q = Mp / Ms
#print( np.percentile(q, [16,50,84] ) )
###############################################################################
#::: translate S19 to allesfitter
###############################################################################
# translate({'a/R_host':3.562, 'R_companion/R_host':0.09716})
###############################################################################
#::: run the fit
###############################################################################
# allesfitter.show_initial_guess('allesfit_sine_physical')
# allesfitter.mcmc_fit('allesfit_sine_physical')
# allesfitter.mcmc_output('allesfit_sine_physical')
# allesfitter.show_initial_guess('allesfit_sine_series')
allesfitter.mcmc_fit('allesfit_sine_series')
allesfitter.mcmc_output('allesfit_sine_series')
def fit(self, candidate_df, star_df, cpus, allesfit_dir):
candidate_row = candidate_df.iloc[0]
sherlock_star_file = self.object_dir + "/params_star.csv"
star_file = allesfit_dir + "/params_star.csv"
params_file = allesfit_dir + "/params.csv"
settings_file = allesfit_dir + "/settings.csv"
if candidate_row["number"] is None or np.isnan(candidate_row["number"]):
lc_file = "/" + candidate_row["lc"]
else:
lc_file = "/" + str(candidate_row["number"]) + "/lc_" + str(candidate_row["curve"]) + ".csv"
shutil.copyfile(self.object_dir + lc_file, allesfit_dir + "/lc.csv")
if os.path.exists(sherlock_star_file) and os.path.isfile(sherlock_star_file):
shutil.copyfile(sherlock_star_file, star_file)
shutil.copyfile(resources_dir + "/resources/allesfitter/params.csv", params_file)
shutil.copyfile(resources_dir + "/resources/allesfitter/settings.csv", settings_file)
fit_width = 0.3333333
if candidate_row["duration"] is not None:
fit_width = float(candidate_row["duration"]) / 60 / 24 * 7
# TODO replace sherlock properties from allesfitter files
# TODO only use params_star when the star mass or radius was not assumed
with open(settings_file, 'r+') as f:
text = f.read()
text = re.sub('\\${sherlock:cores}', str(cpus), text)
text = re.sub('\\${sherlock:fit_width}', str(fit_width), text)
text = re.sub('\\${sherlock:name}', str(candidate_row["name"]), text)
detrend_param = "baseline_flux_lc,hybrid_offset"
detrend_param = detrend_param if self.detrend else "#" + detrend_param
text = re.sub('\\${sherlock:detrend}', detrend_param, text)
f.seek(0)
f.write(text)
f.truncate()
with open(params_file, 'r+') as f:
text = f.read()
text = re.sub('\\${sherlock:t0}', str(candidate_row["t0"]), text)
text = re.sub('\\${sherlock:t0_min}', str(candidate_row["t0"] - 0.02), text)
text = re.sub('\\${sherlock:t0_max}', str(candidate_row["t0"] + 0.02), text)
text = re.sub('\\${sherlock:period}', str(candidate_row["period"]), text)
text = re.sub('\\${sherlock:period_min}', str(candidate_row["period"] - candidate_row["per_err"]), text)
text = re.sub('\\${sherlock:period_max}', str(candidate_row["period"] + candidate_row["per_err"]), text)
rp_rs = candidate_row["rp_rs"] if candidate_row["rp_rs"] != "-" else 0.1
depth = candidate_row["depth"] / 1000
depth_err = depth * 0.2
rp_rs_err = 0.5 / math.sqrt(depth) * depth_err
text = re.sub('\\${sherlock:rp_rs}', str(rp_rs), text)
# TODO calculate depth error in SHERLOCK maybe given the std deviation from the depths or even using the residuals
rp_rs_min = rp_rs - rp_rs_err
rp_rs_min = rp_rs_min if rp_rs_min > 0 else 0.0000001
text = re.sub('\\${sherlock:rp_rs_min}', str(rp_rs_min), text)
text = re.sub('\\${sherlock:rp_rs_max}', str(rp_rs + rp_rs_err), text)
sum_rp_rs_a = (candidate_row["rp_rs"] + star_df.iloc[0]['R_star']) / candidate_row["a"] * 0.00465047 \
if candidate_row["rp_rs"] != "-" else 0.2
rp_err_min = depth ** 0.5 * star_df.iloc[0]["R_star_lerr"] + star_df.iloc[0]["R_star"] / 2 * depth ** (- 0.5)
rp_err_max = depth ** 0.5 * star_df.iloc[0]["R_star_uerr"] + star_df.iloc[0]["R_star"] / 2 * depth ** (- 0.5)
constant = (6.674e-11 / 4 / (math.pi ** 2)) ** (1 / 3) / 1.48e11
mstar = star_df.iloc[0]["M_star"] * 2e30
mstar_low_err = star_df.iloc[0]["M_star_lerr"] * 2e30
mstar_up_err = star_df.iloc[0]["M_star_uerr"] * 2e30
per = candidate_row["period"] * 24 * 3600
per_err = candidate_row["per_err"] * 24 * 3600
a_err_min = constant * ((mstar ** (1/3)) * 2 / 3 * (per ** (-2/3)) * per_err + per ** (2/3) / 3 * (mstar ** (-2/3)) * mstar_low_err)
a_err_max = constant * ((mstar ** (1/3)) * 2 / 3 * (per ** (-2/3)) * per_err + per ** (2/3) / 3 * (mstar ** (-2/3)) * mstar_up_err)
a_err_min_rads = a_err_min * 215
a_err_max_rads = a_err_max * 215
a_rads = candidate_row["a"] * 215
radp_rads = candidate_row["rad_p"] / 0.0091577
sum_rp_rs_a_min_err = 1 / a_rads * rp_err_min + 1 / a_rads * star_df.iloc[0]["R_star_lerr"] + \
(radp_rads + star_df.iloc[0]["R_star"]) / (a_rads ** 2) * a_err_min_rads
sum_rp_rs_a_max_err = 1 / a_rads * rp_err_max + 1 / a_rads * star_df.iloc[0]["R_star_uerr"] + \
(radp_rads + star_df.iloc[0]["R_star"]) / (a_rads ** 2) * a_err_max_rads
sum_rp_rs_a_min_err_aus = sum_rp_rs_a_min_err / 215
sum_rp_rs_a_max_err_aus = sum_rp_rs_a_max_err / 215
sum_rp_rs_a_min = sum_rp_rs_a - sum_rp_rs_a_min_err_aus
sum_rp_rs_a_min = sum_rp_rs_a_min if sum_rp_rs_a_min > 0 else 0.0000001
sum_rp_rs_a_max = sum_rp_rs_a + sum_rp_rs_a_max_err_aus
text = re.sub('\\${sherlock:sum_rp_rs_a}', str(sum_rp_rs_a), text)
text = re.sub('\\${sherlock:sum_rp_rs_a_min}', str(sum_rp_rs_a_min), text)
text = re.sub('\\${sherlock:sum_rp_rs_a_max}', str(sum_rp_rs_a_max), text)
text = re.sub('\\${sherlock:name}', str(candidate_row["name"]), text)
if os.path.exists(sherlock_star_file) and os.path.isfile(sherlock_star_file):
text = re.sub('\\${sherlock:ld_a}', str(star_df.iloc[0]["ld_a"]) + ",0", text)
text = re.sub('\\${sherlock:ld_b}', str(star_df.iloc[0]["ld_b"]) + ",0", text)
else:
text = re.sub('\\${sherlock:ld_a}', "0.5,1", text)
text = re.sub('\\${sherlock:ld_b}', "0.5,1", text)
f.seek(0)
f.write(text)
f.truncate()
allesfitter.show_initial_guess(allesfit_dir)
self.custom_plot(candidate_row["name"], candidate_row["period"], fit_width, allesfit_dir, "initial_guess")
if not self.only_initial:
if not self.mcmc:
allesfitter.ns_fit(allesfit_dir)
allesfitter.ns_output(allesfit_dir)
elif self.mcmc:
allesfitter.mcmc_fit(allesfit_dir)
allesfitter.mcmc_output(allesfit_dir)
self.custom_plot(candidate_row["name"], candidate_row["period"], fit_width, allesfit_dir, "posterior")