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prepare_spectra.py
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prepare_spectra.py
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from __future__ import print_function
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
from shutil import copyfile
from astropy.table import Table
from pyraf import iraf
from glob import glob
from disentangle_spectra_functions import _spectra_normalize, _go_to_dir
def export_observation_to_txt(fits_path, txt_path):
print(' Exporting file')
for order in np.arange(1, 32, 1):
try:
iraf.wspectext(input=fits_path+'[*,'+str(order)+',1]', output=txt_path+'_{:.0f}.txt'.format(order), header='no')
except Exception as e:
print(e)
pass
iraf.noao(_doprint=0, Stdout="/dev/null")
iraf.rv(_doprint=0, Stdout="/dev/null")
iraf.imred(_doprint=0, Stdout="/dev/null")
iraf.ccdred(_doprint=0, Stdout="/dev/null")
iraf.images(_doprint=0, Stdout="/dev/null")
iraf.immatch(_doprint=0, Stdout="/dev/null")
iraf.onedspec(_doprint=0, Stdout="/dev/null")
iraf.twodspec(_doprint=0, Stdout="/dev/null")
iraf.apextract(_doprint=0, Stdout="/dev/null")
iraf.imutil(_doprint=0, Stdout="/dev/null")
iraf.echelle(_doprint=0, Stdout="/dev/null")
iraf.astutil(_doprint=0, Stdout="/dev/null")
iraf.apextract.dispaxi = 1
iraf.echelle.dispaxi = 1
iraf.ccdred.instrum = 'blank.txt'
os.environ['PYRAF_BETA_STATUS'] = '1'
os.system('mkdir uparm')
iraf.set(uparm=os.getcwd() + '/uparm')
data_dir = '/data4/travegre/Projects/Asiago_binaries/'
reduc_dir = '/data4/travegre/Projects/Echelle_Asiago_Reduction/delo/observations/'
obs_metadata = Table.read(data_dir + 'star_data_all.csv')
obs_metadata = obs_metadata[obs_metadata['odkdaj'] == 'nova']
_go_to_dir('Binaries_spectra')
copyfile(data_dir + 'star_data_all.csv', 'star_data_all.csv')
stars = ['TV_LMi', 'GZ_Dra', 'V455_Aur', 'GK_Dra', 'DT_Cam', 'V394_Vul', 'CI_CVn', 'V1898_Cyg', 'V417_Aur', 'EQ_Boo', 'V994_Her', 'CN_Lyn', 'DV_Cam'][6:15]
for star in stars:
print('Working on star ' + star)
star_obs = obs_metadata[obs_metadata['star'] == star.replace('_', ' ').lower()]
source_folder = ['binaries_13_' + dd[:4] + dd[5:7] for dd in star_obs['dateobs']]
star_obs['source_folder'] = source_folder
print(star_obs[np.argsort(star_obs['JD'])])
_go_to_dir(star)
# remove everything
os.system('rm -R *')
_go_to_dir('spec')
n_no_spec = 0
n_no_wvl = 0
for i_s in range(len(star_obs)):
star_spec = star_obs[i_s]
spec_name = star_spec['filename']
spec_suff = '.ec.vh'
print(spec_name)
# does reduced data exist
targ_dir = reduc_dir + star_spec['source_folder'] + '/' + spec_name + spec_suff + '.fits'
if not os.path.isfile(targ_dir):
# check date foders before and after
year = int(star_spec['source_folder'][-6:-2])
month = int(star_spec['source_folder'][-2:])
if month == 12:
date_plus = '{:04d}{:02d}'.format(year+1, 1)
else:
date_plus = '{:04d}{:02d}'.format(year, month+1)
if month == 1:
date_minus = '{:04d}{:02d}'.format(year-1, 12)
else:
date_minus = '{:04d}{:02d}'.format(year, month-1)
targ_dir_m = reduc_dir + star_spec['source_folder'][:-6] + date_minus + '/' + spec_name + spec_suff + '.fits'
targ_dir_p = reduc_dir + star_spec['source_folder'][:-6] + date_plus + '/' + spec_name + spec_suff + '.fits'
if os.path.isfile(targ_dir_m):
print(' Found at previous date - ' + targ_dir_m)
targ_dir = targ_dir_m
elif os.path.isfile(targ_dir_p):
print(' Found at latter date - ' + targ_dir_p)
targ_dir = targ_dir_p
else:
print(' Not found ' + targ_dir)
n_no_spec += 1
continue
# copy reduced data
copyfile(targ_dir, spec_name + spec_suff + '.fits')
_go_to_dir(spec_name + spec_suff)
export_observation_to_txt(targ_dir, spec_name + spec_suff)
# normalise spectra
print(' Normalising spectra')
bad_wvl_order = 0
no_data_order = 0
for txt_file in glob(spec_name + '*_*.txt'):
txt_out = txt_file[:-4] + '_normalised.txt'
order_data = np.loadtxt(txt_file)
if len(order_data) == 0:
print(' No data in order ' + txt_file)
no_data_order += 1
continue
# crop order data to remove noisy part of the echelle order
n_wvl_rem1 = 50
order_data = order_data[n_wvl_rem1:-n_wvl_rem1, :]
n_data = order_data.shape[0]
ref_flx_norm_curve1 = _spectra_normalize(np.arange(n_data), order_data[:, 1],
steps=10, sigma_low=2., sigma_high=2.5, n_min_perc=8.,
order=11, func='cheb', return_fit=True, median_init=False)
ref_flx_norm_curve2 = _spectra_normalize(np.arange(n_data), order_data[:, 1]/ref_flx_norm_curve1,
steps=10, sigma_low=2., sigma_high=2.5, n_min_perc=8.,
order=3, func='cheb', return_fit=True, median_init=True)
# extra wvl border removal
n_wvl_rem2 = 100
order_data = order_data[n_wvl_rem2:-n_wvl_rem2, :]
ref_flx_norm_curve1 = ref_flx_norm_curve1[n_wvl_rem2:-n_wvl_rem2]
ref_flx_norm_curve2 = ref_flx_norm_curve2[n_wvl_rem2:-n_wvl_rem2]
# renorm order
fig, ax = plt.subplots(3, 1, sharex=True, figsize=(13, 7))
ax[0].plot(order_data[:, 0], order_data[:, 1])
ax[0].plot(order_data[:, 0], ref_flx_norm_curve1)
ax[1].plot(order_data[:, 0], order_data[:, 1] / ref_flx_norm_curve1)
ax[1].plot(order_data[:, 0], ref_flx_norm_curve2)
ax[2].plot(order_data[:, 0], order_data[:, 1] / ref_flx_norm_curve1 / ref_flx_norm_curve2)
ax[1].set(ylim=(0.3, 1.2))
ax[2].set(ylim=(0.3, 1.2), xlim=(order_data[0, 0], order_data[-1, 0]))
fig.tight_layout()
fig.subplots_adjust(hspace=0, wspace=0)
fig.savefig(txt_file[:-4] + '_normalised.png')
plt.close(fig)
if order_data[1, 0] - order_data[0, 0] == 1:
print(' No wav cal in ' + txt_file)
bad_wvl_order += 1
continue
order_data[:, 1] = order_data[:, 1] / ref_flx_norm_curve1
np.savetxt(txt_out, order_data, fmt=['%.5f', '%.5f'])
if bad_wvl_order > 20:
n_no_wvl += 1
os.chdir('..')
print("Stats for {:s} are -> exposures: {:.0f} no-data: {:.0f} no-wvl: {:.0f} ok: {:.0f}".format(star, len(star_obs), n_no_spec, n_no_wvl, len(star_obs) - n_no_wvl - n_no_spec))
os.chdir('..')
os.chdir('..')
print('==========================================================================')
print('==========================================================================')
print('\n \n \n')