import sys

import os

import numpy as np

from astropy.io import fits

from astropy import units as u

import ccdproc

from ccdproc import CCDData

from datetime import datetime

from ccdproc import ImageFileCollection

if len(sys.argv)!=2:

print('Usage:\npython wht_calibrate_objects.py [full_path_to_reduced_data]\n')

exit()

indir = sys.argv[1]

os.chdir(indir)

#change this to point to your raw data directory

ic1 = ImageFileCollection(indir.replace('sci2', '20160115'))

#create an array of dates

arm = 'Red arm'

file_list = []

date_list=[]

for hdu, fname in ic1.hdus(obstype='Arc', isiarm=arm, return_fname=True):

if os.path.isfile('w_arc_'+os.path.basename(fname)):

d = hdu.header['DATE-OBS'] + ' ' + hdu.header['UT']

d = datetime.strptime(d, '%Y-%m-%d %H:%M:%S.%f')

file_list.append(fname)

date_list.append(d)

date_arr = np.array(date_list)

#reduce the object frames

for filename in ic1.files_filtered(obstype='TARGET', isiarm=arm):

hdu = fits.open('obj_' + filename)

#find the closest two arcs in time

#open them, based on the time difference

#average their frames, and then use that as the

#wave map for the object file

date = hdu[0].header['DATE-OBS'] + ' ' + hdu[0].header['UT']

date = datetime.strptime(date, '%Y-%m-%d %H:%M:%S.%f')

d = abs(date_arr - date)

i = d.argmin()

if date_arr[i] < date:

j = i+1

else:

j = i-1

w1 = 1 - 1.0*d[i].seconds/(d[i].seconds+d[j].seconds)

w2 = 1 - 1.0*d[j].seconds/(d[i].seconds+d[j].seconds)

wdata1 = fits.open('w_arc_' + os.path.basename(file_list[i]))['WAV'].data

wdata2 = fits.open('w_arc_' + os.path.basename(file_list[j]))['WAV'].data

wdata = w1*wdata1 + w2*wdata2

hdu.append(fits.ImageHDU(data=wdata, name='WAV'))

hdu.writeto('w_' + os.path.basename(filename), clobber=True)