/
lnacoudetasks.py
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/
lnacoudetasks.py
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#!/usr/bin/env python
#
# A script with tasks to reduce data from the Coude Spectrograph
# at the 1.6m telescope of the Observatorio do Pico dos Dias - Brazil
# Load Python standard modules
import glob
# Load third-party modules
from pyraf import iraf
def checkfile(filename):
'''Print statistics and run open imexamine task'''
iraf.imstatistics.unlearn()
iraf.imexamine.unlearn()
print 'Check output file:'
iraf.imstatistics(filename)
print ' Running "imexamine" task..'
iraf.imexamine(filename, 1)
def masterbias(biasre, output='Zero', combine='median', reject='minmax',
ccdtype='', rdnoise='rdnoise', gain='gain'):
'''run the task ccdred.zerocombine with chosen parameters
Input:
-------
str biasre: regular expression to identify zero level images
Output:
-------
file Zero.fits: combined zerolevel images
'''
biaslist = glob.glob(biasre)
biasstring = ', '.join(biaslist)
# load packages
iraf.imred()
iraf.ccdred()
# unlearn settings
iraf.imred.unlearn()
iraf.ccdred.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.ccdred.combine.unlearn()
iraf.ccdred.zerocombine.unlearn()
iraf.ccdred.setinstrument.unlearn()
# setup task
iraf.ccdred.zerocombine.output = output
iraf.ccdred.zerocombine.combine = combine
iraf.ccdred.zerocombine.reject = reject
iraf.ccdred.zerocombine.ccdtype = ccdtype
iraf.ccdred.zerocombine.rdnoise = rdnoise
iraf.ccdred.zerocombine.gain = gain
# run task
iraf.ccdred.zerocombine(input=biasstring)
def masterflat(flatre, output='Flat', combine='median', reject='sigclip',
scale='mode', rdnoise='rdnoise', gain='gain'):
'''combine flat images with the task ccdred.flatcombine
Input:
-------
str: flatre - regular expression to bias files in the current directory
Output:
-------
file: Flat.fits - combined flat field images
'''
flatlist = glob.glob(flatre)
flatstring = ', '.join(flatlist)
# load packages
iraf.imred()
iraf.ccdred()
# unlearn settings
iraf.imred.unlearn()
iraf.ccdred.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.ccdred.combine.unlearn()
iraf.ccdred.flatcombine.unlearn()
iraf.ccdred.setinstrument.unlearn()
# setup task
iraf.ccdred.flatcombine.output = output
iraf.ccdred.flatcombine.combine = combine
iraf.ccdred.flatcombine.reject = reject
iraf.ccdred.flatcombine.ccdtype = ''
iraf.ccdred.flatcombine.process = 'no'
iraf.ccdred.flatcombine.subsets = 'yes'
iraf.ccdred.flatcombine.scale = scale
iraf.ccdred.flatcombine.rdnoise = rdnoise
iraf.ccdred.flatcombine.gain = gain
iraf.ccdred.flatcombine(input=flatstring)
def subzero(imagesre, zero='Zero'):
'''Run ccdproc remove Zero level noise'''
imageslist = glob.glob(imagesre)
imagesin = ', '.join(imageslist)
# Load packages
iraf.imred()
iraf.ccdred()
# Unlearn previouse settings
iraf.ccdred.ccdproc.unlearn()
iraf.ccdred.combine.unlearn()
# setup and run task
iraf.ccdred.ccdproc.ccdtype = ''
iraf.ccdred.ccdproc.noproc = False
iraf.ccdred.ccdproc.fixpix = False
iraf.ccdred.ccdproc.overscan = False
iraf.ccdred.ccdproc.darkcor = False
iraf.ccdred.ccdproc.illumcor = False
iraf.ccdred.ccdproc.fringecor = False
iraf.ccdred.ccdproc.readcor = False
iraf.ccdred.ccdproc.scancor = False
iraf.ccdred.ccdproc.trim = False
iraf.ccdred.ccdproc.trimsec = ''
iraf.ccdred.ccdproc.readaxis = 'line'
iraf.ccdred.ccdproc.zerocor = True
iraf.ccdred.ccdproc.zero = zero
iraf.ccdred.ccdproc.flatcor = False
iraf.ccdred.ccdproc.flat = ''
iraf.ccdred.ccdproc(images=imagesin)
def divflat(imagesre, flat='Flat'):
'''Run ccdproc task to images'''
imageslist = glob.glob(imagesre)
imagesin = ', '.join(imageslist)
# Load packages
iraf.imred()
iraf.ccdred()
# Unlearn settings
iraf.ccdred.ccdproc.unlearn()
iraf.ccdred.combine.unlearn()
# Setup and run task
iraf.ccdred.ccdproc.ccdtype = ''
iraf.ccdred.ccdproc.noproc = False
iraf.ccdred.ccdproc.fixpix = False
iraf.ccdred.ccdproc.overscan = False
iraf.ccdred.ccdproc.darkcor = False
iraf.ccdred.ccdproc.illumcor = False
iraf.ccdred.ccdproc.fringecor = False
iraf.ccdred.ccdproc.readcor = False
iraf.ccdred.ccdproc.scancor = False
iraf.ccdred.ccdproc.trim = False
iraf.ccdred.ccdproc.trimsec = ''
iraf.ccdred.ccdproc.readaxis = 'line'
iraf.ccdred.ccdproc.zerocor = False
iraf.ccdred.ccdproc.zero = ''
iraf.ccdred.ccdproc.flatcor = True
iraf.ccdred.ccdproc.flat = flat
iraf.ccdred.ccdproc(images=imagesin)
def correctimages(imagesre, zero='Zero', flat='nFlat'):
'''Run ccdproc task to correct images'''
imageslist = glob.glob(imagesre)
imagesin = ', '.join(imageslist)
trimsection = str(raw_input('Enter trim section (or Hit <Enter>): '))
trimquery = True
if trimsection == '':
trimquery = False
# Load Packages
iraf.imred()
iraf.ccdred()
# Unlearn Settings
iraf.ccdred.ccdproc.unlearn()
iraf.ccdred.combine.unlearn()
# Setup and run task
iraf.ccdred.ccdproc.ccdtype = ''
iraf.ccdred.ccdproc.noproc = False
iraf.ccdred.ccdproc.fixpix = False
iraf.ccdred.ccdproc.overscan = False
iraf.ccdred.ccdproc.darkcor = False
iraf.ccdred.ccdproc.illumcor = False
iraf.ccdred.ccdproc.fringecor = False
iraf.ccdred.ccdproc.readcor = False
iraf.ccdred.ccdproc.scancor = False
iraf.ccdred.ccdproc.trim = trimquery
iraf.ccdred.ccdproc.trimsec = trimsection
iraf.ccdred.ccdproc.readaxis = 'line'
iraf.ccdred.ccdproc.zerocor = True
iraf.ccdred.ccdproc.zero = zero
iraf.ccdred.ccdproc.flatcor = True
iraf.ccdred.ccdproc.flat = flat
iraf.ccdred.ccdproc(images=imagesin)
def runapall(imagesre, gain='gain', rdnoise='rdnoise'):
'''Extract aperture spectra for science images ...'''
imageslist = glob.glob(imagesre)
imagesin = ', '.join(imageslist)
# load packages
iraf.imred()
iraf.ccdred()
iraf.specred()
# unlearn previous settings
iraf.ccdred.combine.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.specred.apall.unlearn()
# setup and run task
iraf.specred.apall.format = 'onedspec'
iraf.specred.apall.readnoise = rdnoise
iraf.specred.apall.gain = gain
iraf.specred.apall(input=imagesin)
def flatresponse(input='Flat', output='nFlat'):
''' normalize Flat to correct illumination patterns'''
iraf.imred()
iraf.ccdred()
iraf.specred()
iraf.ccdred.combine.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.specred.response.unlearn()
iraf.specred.response.interactive = True
iraf.specred.response.function = 'chebyshev'
iraf.specred.response.order = 1
iraf.specred.response(calibration=input, normalization=input,
response=output)
def flatresponse2(input='Flat_zero', output='nFlat'):
''' normalize Flat to correct illumination patterns'''
iraf.boxcar(input, 'Flatboxcar', 10, 10)
iraf.imarith(input, '/', 'Flatboxcar', output)
def scalewavelenght(calspec):
''' Creates a wavelenght solution for 'calspec' '''
iraf.imred()
iraf.specred()
linelist = str(raw_input('Enter file with list of lines (linelists$thar.dat) : '))
if linelist == '':
linelist = 'linelists$thar.dat'
iraf.specred.identify.coordlist = linelist
iraf.specred.identify(images=calspec)
def applywavesolution(inputre, calspec):
''' apply calibration solution to science spectra '''
inputlist = glob.glob(inputre)
inputstring = ', '.join(inputlist)
outputstring = ', '.join([inp[:-5]+'_spec.fits' for inp in inputlist])
iraf.hedit.unlearn()
iraf.dispcor.unlearn()
iraf.hedit.fields = 'REFSPEC1'
iraf.hedit.value = calspec
iraf.hedit.add = True
iraf.hedit(images=inputstring)
iraf.dispcor(input=inputstring, output=outputstring)
def combinespecs(inputre, scale='exposure', rdnoise='rdnoise', gain='gain'):
''' combine two or more spectra tha matches the input regular expression
'''
specfiles = glob.glob(specre)
specstring = ', '.join(specfiles)
print 'The following spectra will be combined: '
print specfiles
specout = str(raw_input('Enter output file name: '))
iraf.scombine.unlearn()
iraf.scombine.scale = scale
iraf.scombine.rdnoise = rdnoise
iraf.scombine.gain = gain
iraf.scombine(input=specstring, output=specout)
ask = str(raw_input('Plot output with splot? Y/N: '))
if (ask == 'y') or (ask == 'Y'):
iraf.splot.unlearn()
iraf.splot(specout)