forked from evandromr/scripts_pyraf
/
opd_coude.py
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/
opd_coude.py
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from pyraf import iraf
from ds9 import ds9
from astropy.io import fits
import matplotlib.pyplot as plt
import numpy as np
import glob
science = str(raw_input('Science images: '))
cal = str(raw_input('Calibration images: '))
sciimages = glob.glob(science)
flatcup = str(raw_input('Flat cupula: '))
flati = str(raw_input('Flat interno: '))
bias = str(raw_input('Zero level: '))
linelist = str(raw_input('Lista de linhas (linelists$thar.dat): '))
iraf.imred()
iraf.ccdred()
iraf.specred()
ds9()
iraf.zerocombine.unlearn()
iraf.zerocombine(input=bias, reject='avsigclip', ccdtype='',
rdnoise='rdnoise', gain='gain')
iraf.imstat('bias*')
iraf.imstat('Zero')
iraf.imexamine('Zero')
iraf.flatcombine.unlearn()
iraf.flatcombine(input=flatcup, output='Flat', ccdtype='', process=False,
subsets=False, rdnoise='rdnoise', gain='gain')
iraf.flatcombine(input=flati, output='iFlat', ccdtype='', process=False,
subsets=False, rdnoise='rdnoise', gain='gain')
iraf.imstat(flatcup)
iraf.imstat('Flat')
iraf.imstat(flati)
iraf.imstat('iFlat')
iraf.imexamine('Flat')
iraf.imexamine('iFlat')
iraf.response.unlearn()
iraf.response(calibration='Flat', normalization='Flat', response='nFlat',
function='legendre', order='1')
iraf.response.unlearn()
iraf.response(calibration='iFlat', normalization='iFlat', response='niFlat',
function='legendre', order='1')
iraf.imstat('nFlat')
iraf.imexamine('nFlat')
iraf.imstat('niFlat')
iraf.imexamine('niFlat')
iraf.ccdproc.unlearn()
iraf.ccdproc(images=science, ccdtype='', fixpix=False, overscan=False,
darkcor=False, trim=True, zerocor=True, flatcor=True,
trimsec='[*,20:4600]', zero='Zero', flat='nFlat')
iraf.ccdproc(images=cal, ccdtype='', fixpix=False, overscan=False,
darkcor=False, trim=True, zerocor=True, flatcor=True,
trimsec='[*,20:4600]', zero='Zero', flat='niFlat')
iraf.ccdlist('Zero, nFlat, niFlat, {0}, {1}'.format(science, cal))
iraf.imstat('Zero')
iraf.imstat('nFlat')
iraf.imstat(science)
iraf.imstat(cal)
ref = str(raw_input('Imagen de referencia (eg. hd161103_0001.fits):'))
iraf.imexamine(ref)
iraf.apall.unlearn()
iraf.apall(input=science, format='onedspec', readnoise='rdnoise',
gain='gain')
iraf.apall(input=cal, format='onedspec', reference=ref,
readnoise='rdnoise', gain='gain')
iraf.identify(images=cal[:-5]+'*.0001.fits', coordlist=linelist)