ewithlc.py

import os
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['NUMEXPR_NUM_THREADS'] = '1'
os.environ['OMP_NUM_THREADS'] = '1'

import numpy as np
import lightkurve as lk
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import scipy.signal as sps
import astropy.timeseries as ts
from astropy.io import fits
import sys, time, argparse, eleanor
import glob as glob
from spinneret import *

parser = argparse.ArgumentParser(description='Run eleanor and then spinneret')
parser.add_argument('-t', '--tic', required=True, type=int, help='TESS ID')
parser.add_argument('-s', '--sector', required=True, type=int, help='TESS sector')
parser.add_argument('-p', '--gtp', required=False, type=float, default=0, help='Ground truth period')

params = parser.parse_args()

tid = params.tic
sec = params.sector
gtp = params.gtp

id_prepend = 'tic'
file_append = 'eleanor'
cadence = 1/24/2 # 30min

# failed = []

# directorymaker('figs')

wd = os.getcwd()
dir_name = 'kelt_eleanor2'
directorymaker(dir_name)

# prime_sample = np.loadtxt('sectors_control.dat', delimiter=',')
#
# already_done = glob.glob('./{dir_name}/*.csv')

# for i in range(prime_sample.shape[0]):

# tid = int(prime_sample[i][0])
# sec = int(prime_sample[i][1])
# gtp = prime_sample[i][2]

# check if the star has already been processed
# checklist = glob.glob(f'{wd}/{dir_name}/*')
# if f'{wd}/{dir_name}/tic{tid}_s{sec}_eleanor.csv' in checklist:
#     sys.exit()

try:

    os.chdir(dir_name)
    f = np.loadtxt(f'tic{tid}_s{sec}_{file_append}_lc.dat', delimiter=',')
    os.chdir('..')

    time = f[:,0]
    flux = f[:,1]

    p_grid = np.linspace(0.001, 30, 5000)
    freq = 1/p_grid

    time, flux = nancleaner2d(time, flux)
    time, flux = clip(time, flux, 3) #3 sigma clip
    flux = lk.LightCurve(time=time, flux=flux).normalize().flux.value - 1

    target = Spinner(time, flux)

    ps = ts.LombScargle(time, flux).power(freq)
    # freq, ps = ts.LombScargle(time, flux).autopower(method='fastchi2', maximum_frequency=200, samples_per_peak=20)
    target.ls_one_term(freq, ps)

    ps = ts.LombScargle(time, flux, nterms=2).power(freq)
    # freq, ps = ts.LombScargle(time, flux, nterms=2).autopower(method='fastchi2', maximum_frequency=200, samples_per_peak=20)
    target.ls_two_term(freq, ps)

    lags_raw, acf_raw, lags, acf, _x, _y = simple_acf(time, flux, cadence, width=16)
    target.acf(lags, acf)

    fig1 = target.diagnostic_plot(heading=f'TIC {tid} s{sec} // KELT period: {gtp:.2f}d')
    figsaver(fig1, f'TIC{tid}_{file_append}.png')
    filemaker(target, tid, gtp, filename=f'{id_prepend}{tid}_s{sec}_{file_append}.csv', filepath=f'./{dir_name}')

    print(f'{tid} done')

except:
    # failed.append([tid, sec])
    print(f'{tid} failed')

# np.savetxt('failed_control.dat', failed, delimiter=',')