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utils.py
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utils.py
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import numpy as np
import sys, os
import urllib.parse as urlparse
import requests
from bs4 import BeautifulSoup
import math
from tqdm import trange
from astropy.io import fits
from astropy import units as u
from astropy.coordinates import SkyCoord
from astroquery.mast import Tesscut
from datetime import datetime
def listFD(url, ext=''):
"""
Finds the co-trending basis vector files in the directory structure at STScI
Input
-----
type(url) == str
"""
page = requests.get(url).text
soup = BeautifulSoup(page, 'html.parser')
return [url + node.get('href') for node in soup.find_all('a') if node.get('href').endswith(ext)]
def download_cbvs(sector):
"""
Downloads the co-trending basis vectors for a given list of sectors
Input
-----
type(sectors) == int
"""
sector = int(sector)
if sector <= 6:
year = 2018
else:
year = 2019
url = 'https://archive.stsci.edu/missions/tess/ffi/s{0:04d}/{1}/'.format(sector, year)
directs = []
for file in listFD(url):
directs.append(file)
directs = np.sort(directs)[1::]
subdirects = []
for file in listFD(directs[0]):
subdirects.append(file)
subdirects = np.sort(subdirects)[1:-4]
fns = []
for i in range(len(subdirects)):
file = listFD(subdirects[i], ext='cbv.fits')[0]
os.system('curl -O -L {}'.format(file))
fns.append(file.split('/')[-1])
return fns
def convolve_cbvs(sectors=np.arange(1,14,1)):
"""
Bins the co-trending basis vectors into FFI times;
Calls download_cbvs to get filenames
Input
-----
type(sectors) == list
"""
# Gets the cutout for a target in the CVZ
coord = SkyCoord('04:35:50.330 -64:01:37.33', unit=(u.hourangle, u.deg))
sector_table = Tesscut.get_sectors(coord)
for sector in sectors:
files = download_cbvs(int(sector))
manifest = Tesscut.download_cutouts(coordinates=coord, size=31, sector=sector)
cutout = fits.open(manifest['Local Path'][0])
time = cutout[1].data['TIME'] - cutout[1].data['TIMECORR']
for c in trange(len(files)):
cbv = fits.open(files[c])
camera = cbv[1].header['CAMERA']
ccd = cbv[1].header['CCD']
cbv_time = cbv[1].data['Time']
new_fn = './eleanor/metadata/s{0:04d}/cbv_components_s{0:04d}_{1:04d}_{2:04d}.txt'.format(sector, camera, ccd)
convolved = np.zeros((len(time), 16))
for i in range(len(time)):
g = np.argmin( np.abs(time[i] - cbv_time) )
for j in range(16):
index = 'VECTOR_{0}'.format(j+1)
cads = np.arange(g-7, g+8, 1)
convolved[i,j] = np.mean(cbv[1].data[index][cads])
np.savetxt(new_fn, convolved)
def set_quality_flags(sector=np.arange(1,14,1)):
""" Uses the quality flags in a 2-minute target to create quality flags
in the postcards.
We create our own quality flag as well, using our pointing model.
"""
coord = SkyCoord('04:35:50.330 -64:01:37.33', unit=(u.hourangle, u.deg))
sector_table = Tesscut.get_sectors(coord)
manifest = Tesscut.download_cutouts(coordinates=coord, size=31, sector=sector)
cutout = fits.open(manifest['Local Path'][0])
ffi_time = cutout[1].data['TIME'] - cutout[1].data['TIMECORR']
shortCad_fn = 'eleanor/metadata/s{0:04d}/target_s{0:04d}.fits'.format(sector)
# Binary string for values which apply to the FFIs
ffi_apply = int('100010101111', 2)
# Obtains information for 2-minute target
twoMin = fits.open(shortCad_fn)
twoMinTime = twoMin[1].data['TIME']-twoMin[1].data['TIMECORR']
finite = np.isfinite(twoMinTime)
twoMinQual = twoMin[1].data['QUALITY']
twoMinTime = twoMinTime[finite]
twoMinQual = twoMinQual[finite]
convolve_ffi = []
nodata = np.zeros_like(ffi_time)
for i in range(len(ffi_time)):
where = np.where(np.abs(ffi_time[i] - twoMinTime) == np.min(np.abs(ffi_time[i] - twoMinTime)))[0][0]
sflux = np.sum(cutout[1].data['FLUX'][i])
if sflux == 0:
nodata[i] = 4096
if (ffi_time[i] > 1420) and (ffi_time[i] < 1424):
nodata[i] = 4096
v = np.bitwise_or.reduce(twoMinQual[where-7:where+8])
convolve_ffi.append(v)
convolve_ffi = np.array(convolve_ffi)
flags = np.bitwise_and(convolve_ffi, ffi_apply)
np.savetxt('eleanor/metadata/s{0:04d}/quality_s{0:04d}.txt'.format(sector), flags+nodata, fmt='%i')
return flags
def create_ffiindex(sectors=np.arange(1,14,1)):
"""
Creates FFIINDEX for a given sector
Input
-----
type(sectors) == list
"""
def hmsm_to_days(hour=0,min=0,sec=0,micro=0):
"""
Convert hours, minutes, seconds, and microseconds to fractional days.
"""
days = sec + (micro / 1.e6)
days = min + (days / 60.)
days = hour + (days / 60.)
return days / 24.
def date_to_jd(year,month,day):
"""
Convert a date to Julian Day.
"""
if month == 1 or month == 2:
yearp = year - 1
monthp = month + 12
else:
yearp = year
monthp = month
# this checks where we are in relation to October 15, 1582, the beginning
# of the Gregorian calendar.
if ((year < 1582) or
(year == 1582 and month < 10) or
(year == 1582 and month == 10 and day < 15)):
# before start of Gregorian calendar
B = 0
else:
# after start of Gregorian calendar
A = math.trunc(yearp / 100.)
B = 2 - A + math.trunc(A / 4.)
if yearp < 0:
C = math.trunc((365.25 * yearp) - 0.75)
else:
C = math.trunc(365.25 * yearp)
D = math.trunc(30.6001 * (monthp + 1))
jd = B + C + D + day + 1720994.5 + 0.0008 # including leap second correction
return jd
curlfile = 'tesscurl_sector_{0}_ffic.sh'
later_sector_curl = curlfile.format(7)
curr_sector_curl = curlfile.format(sectors)
os.system('curl -O -L https://archive.stsci.edu/missions/tess/download_scripts/sector/{0}'.format(later_sector_curl))
for sector in sectors:
os.system('curl -O -L https://archive.stsci.edu/missions/tess/download_scripts/sector/{0}'.format(curr_sector_curl))
index_sector = open('/Users/AdinaFeinstein/Documents/ELLIE/metadata/s0007/{0}'.format(later_sector_curl))
download_file = []
for line in index_sector:
if len(line) > 30:
download_file.append(line)
break
download_file = download_file[-1]
os.system(download_file)
fn = download_file.split(' ')[5]
a = fits.open(fn)
outarr =np.array([])
indexlist = open('/Users/AdinaFeinstein/Documents/ELLIE/metadata/s{0:04d}/{1}'.format(sector, curr_sector_curl))
for line in indexlist:
if len(line) > 30:
outarr = np.append(outarr, (line.split('tess')[1][0:13]))
times = np.sort(np.unique(outarr))
outarr = np.zeros_like(times, dtype=int)
for i in range(len(times)):
date = datetime.strptime(str(times[i]), '%Y%j%H%M%S')
days = date.day + hmsm_to_days(date.hour,date.minute,date.second,date.microsecond)
tjd = date_to_jd(date.year,date.month,days) - 2457000
cad = (tjd - a[0].header['tstart'])/(30./1440.)
outarr[i] = (int(np.round(cad))+a[0].header['ffiindex'])
np.savetxt('cadences_s{0:04d}.txt'.format(sector), outarr, fmt='%i')
os.remove(fn)
os.remove(curr_sector_curl)
os.remove(later_sector_curl)
convolve_cbvs(sectors=[11])