def apall_config(
config_file,
config_default='/Users/ando/andry/research/Make_software/saltrss/apall_conf.yml'
):
config = yaml.safe_load(open(config_default))
custom_config = {}
if os.path.exists(config_file):
custom_config = yaml.safe_load(open(config_file))
config = config_merge(custom_config, config)
sections = [
'iraf.apall',
]
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.apextract(_doprint=0)
iraf.apextract.unlearn()
iraf.apall.unlearn()
iraf.apsum.unlearn()
iraf.apextract.setParam('dispaxis', 1)
for section_name in sections:
params = config[section_name].items()
for param_id in params:
eval(section_name).setParam(param_id[0], param_id[1])
return config
def apall(lstfile):
iraf.noao()
iraf.twodspec()
iraf.apextract(dispaxis = 2, database = 'database')
f = open(lstfile)
l = f.readlines()
f.close()
l = [tmp.split('\n')[0] for tmp in l]
path = os.getcwd()
for i in l:
infile = 'wftbo' + i
outfile = 'awftbo' + i
while True:
if os.path.isfile(outfile):
print('remove ' + outfile)
os.remove(outfile)
delfile = path + os.sep + 'database/ap' + infile[0:-5]
if os.path.isfile(delfile):
print('remove ' + delfile)
os.remove(delfile)
iraf.apall(input = infile
, output = outfile, apertures = 2, format = 'multispec'
, references = '', profiles = '', interactive = True
, find = True, recenter = True, resize = False
, edit = True, trace = True, fittrace = True
, extract = True, extras = True, review = True
, line = 'INDEF', nsum = 10, lower = -15.0
, upper = 15.0, apidtable = '', b_function = 'chebyshev'
, b_order = 2, b_sample = '-50:-26,26:50', b_naverage = -25
, b_niterate = 1, b_low_reject = 3.0, b_high_reject = 3.0
, b_grow = 0.0, width = 5.0, radius = 10.0
, threshold = 0.0, nfind = 2, minsep = 5.0
, maxsep = 100000.0, order = 'increasing', aprecenter = ''
, npeaks = 'INDEF', shift = True, llimit ='INDEF'
, ulimit = 'INDEF', ylevel = 0.1, peak = True
, bkg = True, r_grow = 0.0, avglimits = False
, t_nsum = 20, t_step = 10, t_nlost = 3, t_function = 'legendre'
, t_order = 7, t_sample = '*', t_naverage = 1
, t_niterate = 1, t_low_reject = 3.0, t_high_reject = 3.0
, t_grow = 0.0, background = 'fit', skybox = 1
, weights = 'variance', pfit = 'fit1d', clean = True
, saturation = 'INDEF', readnoise = 9.4, gain = 0.35
, lsigma = 4.0, usigma = 4.0, nsubaps = 1)
iraf.flpr()
sspecplot.sspecplot(outfile)
getval = raw_input('Are you need repeat apall,may be clean should be close(r/n)')
if getval != 'r':
break
def apall(ilst, olst):
iraf.noao()
iraf.twodspec()
iraf.apextract(dispaxis=1, database='database')
for i, infile in enumerate(ilst):
outfile = olst[i]
laper, raper, back_samp = -7, 7, '-30:-15,15:30'
while True:
if os.path.isfile(outfile):
print('remove ' + outfile)
os.remove(outfile)
delfile = os.getcwd()+os.sep+'database/ap'+infile[0:-5]
if os.path.isfile(delfile):
print('remove ' + delfile)
os.remove(delfile)
iraf.apall(input=infile, output=outfile, apertures=2,
format='multispec', references='', profiles='',
interactive=True, find=True, recenter=True,
resize=False, edit=True, trace=True, fittrace=True,
extract=True, extras=True, review=True, line='INDEF',
nsum=10, lower=laper, upper=raper, apidtable='',
b_function='chebyshev', b_order=2, b_sample=back_samp,
b_naverage=-25, b_niterate=1, b_low_reject=3.0,
b_high_reject=3.0, b_grow=0.0, width=5.0, radius=10.0,
threshold=0.0, nfind=2, minsep=5.0, maxsep=100000.0,
order='increasing', aprecenter='', npeaks='INDEF',
shift=True, llimit='INDEF', ulimit='INDEF', ylevel=0.1,
peak=True, bkg=True, r_grow=0.0, avglimits=False,
t_nsum=20, t_step=10, t_nlost=3, t_function='legendre',
t_order=12, t_sample='*', t_naverage=1, t_niterate=1,
t_low_reject=3.0, t_high_reject=3.0, t_grow=0.0,
background='median', skybox=1, weights='none',
pfit='fit1d', clean=True, saturation='INDEF',
readnoise='CCDRON', gain='CCDGAIN', lsigma=4.0, usigma=4.0,
nsubaps=1)
iraf.flpr()
getval = raw_input(('Are you need repeat apall,'
'may be clean should be close(r/n)'))
if getval != 'r':
break
def ApallTask(self, InputFile, OutputFile, Fits_Folder, line, nsum = None, lower_lim = None, upper_lim = None, extras = 'yes', Suffix = 'bg'):
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.apextract(_doprint=0)
#Incase no output name is given, we generate one with the provided "preffix" (The defaul format is a_std_wolf.dat)
if OutputFile == None:
OutputFile = self.outputNameGenerator(InputFile + '[1]', Suffix)
ApallConf = self.ApallAttributes(InputFile, OutputFile, Fits_Folder, line, nsum, lower_lim, upper_lim, extras)
#Display the equivalent command in IRAF
Command = self.printIrafCommand('apall', ApallConf)
print '--- Using the command'
print Command
# p = Popen(iraf.twodspec.apextract.apall(**ApallConf), shell=True, stdout=PIPE, stderr=STDOUT)
#Run the task
iraf.twodspec.apextract.apall(**ApallConf)
return OutputFile
import operator
import time
import traceback
from lmfit import minimize, Parameters, report_fit
#import cosmics
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
#fixes a bug with latest versions of iraf
iraf.ccdred.instrum='blank.txt'
os.environ['PYRAF_BETA_STATUS'] = '1'
# REFS NEW: ecEC59550 ecEC59758 ecEC59795 ecEC59842 ecEC59844 ecEC59864 ecEC59866 ecEC59881 ecEC59883 ecEC59885
observations = {
'cemp_cand': {'ORIG_DIR':'/home/nandir/IRAF_Echelle_Asiago/CEMP_obs/201712/',
def load_modules():
# Define a function to load all of the modules so that they don't' import
# unless we need them
global iraf
from pyraf import iraf
iraf.pysalt()
iraf.saltspec()
iraf.saltred()
iraf.set(clobber='YES')
global sys
import sys
global os
import os
global shutil
import shutil
global glob
from glob import glob
global pyfits
import pyfits
global np
import numpy as np
global lacosmicx
import lacosmicx
global interp
from scipy import interp
global signal
from scipy import signal
global ndimage
from scipy import ndimage
global interpolate
from scipy import interpolate
global WCS
from astropy.wcs import WCS
global optimize
from scipy import optimize
global ds9
import pyds9 as ds9
global GaussianProcess
from sklearn.gaussian_process import GaussianProcess
global pandas
import pandas
iraf.onedspec()
iraf.twodspec()
iraf.longslit()
iraf.apextract()
iraf.imutil()
iraf.rvsao(motd='no')
'''
from glob import glob
from pyraf import iraf
iraf.stsdas()
iraf.hst_calib()
iraf.stis()
import pyfits
from numpy import isnan, min, logical_and, bitwise_and, linspace, median, arange, correlate, exp, average
from scipy.interpolate import interp1d
from scipy.integrate import trapz
import os
from scipy.optimize import curve_fit
import stistools
iraf.twodspec()
iraf.apextract()
iraf.set(clobber='yes')
def gauss(x, a, x0, sigma, sky):
return a * exp(-(x - x0)**2 / (2 * sigma**2) + sky)
def tofits(filename, data, hdr=None, clobber=False):
"""simple pyfits wrapper to make saving fits files easier."""
from pyfits import PrimaryHDU, HDUList
hdu = PrimaryHDU(data)
if hdr is not None:
hdu.header = hdr
hdulist = HDUList([hdu])
hdulist.writeto(filename, clobber=clobber, output_verify='ignore')
# SALT/RSS longslit reduction #
# #
#########################################################################################
'''
import os
from pyraf import iraf
from astropy.io import fits
from shutil import copy2
import rsstools as rt
import numpy as np
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.apextract(dispaxis='1')
iraf.stsdas(_doprint=0)
iraf.analysis(_doprint=0)
iraf.fitting(_doprint=0)
class arc(object):
def __init__(self, name):
self.name = name
self.noext = os.path.splitext(self.name)[0]
self.idname = rt.makesetups(self.name,"arc")
self.idfits = self.idname+'.fits'
self.linelist = self.idname.split('_')[0].lower()+'.dat'
self.header = self.read_header()
def read_header(self):
return fits.getheader(self.name)
def extract_spectrum(targetdir,
trace,
arcspec,
refspec,
t_nsum,
t_step,
line,
ylevel,
interactive):
"""
Extract spectrum
Must be in target directory.
"""
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.apextract(_doprint=0)
basedir = '/data/lc585/WHT_20150331/OBS/'
targetdir = os.path.join(basedir,targetdir,'Reduced')
print 'Target directory is ' + targetdir
print 'Extracting spectrum...'
if os.path.exists( os.path.join(targetdir, 'imcomb.ms.fits') ):
os.remove( os.path.join( targetdir, 'imcomb.ms.fits') )
print 'Removing file ' + os.path.join( targetdir, 'imcomb.ms.fits')
# If can't fit trace use trace from nearby object
if trace == 'no':
dest = os.path.join(targetdir,'database')
if not os.path.exists(dest):
os.makedirs(dest)
db = os.path.join(basedir,refspec,'Reduced','database','ap_data_lc585_WHT_20150331_OBS_'+refspec+'_Reduced_imcomb')
shutil.copy(db,dest)
iraf.apall.setParam('references',os.path.join(basedir,refspec,'Reduced','imcomb.fit')) # List of aperture reference images
print 'Using trace from reference spectra ' + refspec
# Since frame is averaged I think we need to scale down read nosie but gain will stay the same.
names = []
for n in os.listdir(targetdir):
if (n.endswith('.fit')) & (n.startswith('r')):
names.append(n)
nframes = float(len(names))
# Doesn't seem to work if I give it absolute path to input!
iraf.apall.setParam('input','imcomb.fit') # List of input images
iraf.apall.setParam('output','') # List of output spectra
iraf.apall.setParam('apertur','') # Apertures
iraf.apall.setParam('format','multispec') # Extracted spectra format
iraf.apall.setParam('referen','') # List of aperture reference images
iraf.apall.setParam('profile','') # List of aperture profile images
iraf.apall.setParam('interac',interactive) # Run task interactively?
iraf.apall.setParam('find','no') # Find apertures?
iraf.apall.setParam('recente','no') # Recenter apertures?
iraf.apall.setParam('resize','no') # Resize apertures?
iraf.apall.setParam('edit','yes') # Edit apertures?
iraf.apall.setParam('trace',trace) # Trace apertures?
iraf.apall.setParam('fittrac',interactive) # Fit the traced points interactively?
iraf.apall.setParam('extract','yes') # Extract spectra?
iraf.apall.setParam('extras','yes') # Extract sky, sigma, etc.?
iraf.apall.setParam('review',interactive) # Review extractions?
iraf.apall.setParam('line',line) # Dispersion line
iraf.apall.setParam('nsum',20) # Number of dispersion lines to sum or median
# DEFAULT APERTURE PARAMETERS
iraf.apall.setParam('lower',-5.) # Lower aperture limit relative to center
iraf.apall.setParam('upper',5.) # Upper aperture limit relative to center
iraf.apall.setParam('apidtab','') # Aperture ID table (optional)
# DEFAULT BACKGROUND PARAMETERS
# Background is now a constant at each wavelength
iraf.apall.setParam('b_funct','chebyshev') # Background function
iraf.apall.setParam('b_order',1) # Background function order
iraf.apall.setParam('b_sampl','-10:-6,6:10') # Background sample regions
iraf.apall.setParam('b_naver',-3) # Background average or median
iraf.apall.setParam('b_niter',2) # Background rejection iterations
iraf.apall.setParam('b_low_r',3.) # Background lower rejection sigma
iraf.apall.setParam('b_high_',3.) # Background upper rejection sigma
iraf.apall.setParam('b_grow',0.) # Background rejection growing radius
# APERTURE CENTERING PARAMETERS
iraf.apall.setParam('width',5.) # Profile centering width
iraf.apall.setParam('radius',10.) # Profile centering radius
iraf.apall.setParam('thresho',0.) # Detection threshold for profile centering
# AUTOMATIC FINDING AND ORDERING PARAMETERS
iraf.apall.setParam('nfind','') # Number of apertures to be found automatically
iraf.apall.setParam('minsep',5.) # Minimum separation between spectra
iraf.apall.setParam('maxsep',100000.) # Maximum separation between spectra
iraf.apall.setParam('order','increasing') # Order of apertures
# RECENTERING PARAMETERS
iraf.apall.setParam('aprecen','') # Apertures for recentering calculation
iraf.apall.setParam('npeaks','INDEF') # Select brightest peaks
iraf.apall.setParam('shift','yes') # Use average shift instead of recentering?
# RESIZING PARAMETERS
iraf.apall.setParam('llimit','INDEF') # Lower aperture limit relative to center
iraf.apall.setParam('ulimit','INDEF') # Upper aperture limit relative to center
iraf.apall.setParam('ylevel',0.2) # Fraction of peak or intensity for automatic widt
iraf.apall.setParam('peak','yes') # Is ylevel a fraction of the peak?
iraf.apall.setParam('bkg','yes') # Subtract background in automatic width?
iraf.apall.setParam('r_grow',0.) # Grow limits by this factor
iraf.apall.setParam('avglimi','no') # Average limits over all apertures?
# TRACING PARAMETERS
iraf.apall.setParam('t_nsum',20) # Number of dispersion lines to sum
iraf.apall.setParam('t_step', 20) # Tracing step
iraf.apall.setParam('t_nlost',3) # Number of consecutive times profile is lost befo
iraf.apall.setParam('t_funct','spline3') # Trace fitting function
iraf.apall.setParam('t_order',2) # Trace fitting function order
iraf.apall.setParam('t_sampl','*') # Trace sample regions
iraf.apall.setParam('t_naver',1) # Trace average or median
iraf.apall.setParam('t_niter',2) # Trace rejection iterations
iraf.apall.setParam('t_low_r',3.) # Trace lower rejection sigma
iraf.apall.setParam('t_high_',3.) # Trace upper rejection sigma
iraf.apall.setParam('t_grow',0.) # Trace rejection growing radius
# EXTRACTION PARAMETERS
iraf.apall.setParam('backgro','none') # Background to subtract
iraf.apall.setParam('skybox',1) # Box car smoothing length for sky
iraf.apall.setParam('weights','variance') # Extraction weights (none|variance)
iraf.apall.setParam('pfit','fit1d') # Profile fitting type (fit1d|fit2d)
iraf.apall.setParam('clean','yes') # Detect and replace bad pixels?
iraf.apall.setParam('saturat',300000.) # Saturation level
# iraf.apall.setParam('readnoi',17.0)
iraf.apall.setParam('readnoi',17./np.sqrt(nframes)) # Read out noise sigma (photons)
iraf.apall.setParam('gain',4.) # Photon gain (photons/data number)
iraf.apall.setParam('lsigma',4.) # Lower rejection threshold
iraf.apall.setParam('usigma',4.) # Upper rejection threshold
iraf.apall.setParam('nsubaps',1) # Number of subapertures per aperture
iraf.apall.setParam('mode','q') # h = hidden, q = query, l = learn
iraf.apall()
# Now extract arc through same aperture for wavelength calibration
print '\n' '\n' '\n'
print 'Extracting Arc through same aperture...'
if os.path.exists( os.path.join(targetdir,'aimcomb.fits')):
os.remove( os.path.join(targetdir, 'aimcomb.fits') )
print 'Removing file ' + os.path.join(targetdir, 'aimcomb.fits')
arcspec = os.path.join(basedir,arcspec)
iraf.apall.setParam('input', arcspec)
iraf.apall.setParam('output', 'aimcomb')
iraf.apall.setParam('references', 'imcomb.fit' )
iraf.apall.setParam('recenter','no')
iraf.apall.setParam('trace','no')
iraf.apall.setParam('background','no')
iraf.apall.setParam('interactive','no')
iraf.apall()
if os.path.exists( os.path.join(targetdir, 'imcomb+bkgd.ms.fits') ):
os.remove( os.path.join( targetdir, 'imcomb+bkgd.ms.fits') )
print 'Removing file ' + os.path.join( targetdir, 'imcomb+bkgd.ms.fits')
iraf.apall.setParam('input','imcomb+bkgd.fit') # List of input images
iraf.apall.setParam('output','') # List of output spectra
iraf.apall.setParam('referen','imcomb.fit') # List of aperture reference images
iraf.apall.setParam('interac','yes') # Run task interactively?
iraf.apall.setParam('find','yes') # Find apertures?
iraf.apall.setParam('recenter','no') # Recenter apertures?
iraf.apall.setParam('resize','no') # Resize apertures?
iraf.apall.setParam('edit','yes') # Edit apertures?
iraf.apall.setParam('trace','no') # Trace apertures?
iraf.apall.setParam('fittrac',interactive) # Fit the traced points interactively?
iraf.apall.setParam('extract','yes') # Extract spectra?
iraf.apall.setParam('extras','yes') # Extract sky, sigma, etc.?
iraf.apall.setParam('review','yes') # Review extractions?
# DEFAULT BACKGROUND PARAMETERS
# Background is now a constant at each wavelength
iraf.apall.setParam('b_funct','chebyshev') # Background function
iraf.apall.setParam('b_order',1) # Background function order
iraf.apall.setParam('b_sampl','-10:-6,6:10') # Background sample regions
iraf.apall.setParam('b_naver',-3) # Background average or median
iraf.apall.setParam('b_niter',2) # Background rejection iterations
iraf.apall.setParam('b_low_r',3.) # Background lower rejection sigma
iraf.apall.setParam('b_high_',3.) # Background upper rejection sigma
iraf.apall.setParam('b_grow',0.) # Background rejection growing radius
# EXTRACTION PARAMETERS
# before i wasn't dividing by the square root of the frames, but surely this must be true if I'm taking the average
iraf.apall.setParam('backgro','median') # Background to subtract
iraf.apall.setParam('skybox',1) # Box car smoothing length for sky
iraf.apall.setParam('weights','variance') # Extraction weights (none|variance)
iraf.apall.setParam('pfit','fit1d') # Profile fitting type (fit1d|fit2d)
iraf.apall.setParam('clean','yes') # Detect and replace bad pixels?
iraf.apall.setParam('saturat',300000.) # Saturation level
# iraf.apall.setParam('readnoi',17.0)
iraf.apall.setParam('readnoi',17.0/np.sqrt(nframes)) # Read out noise sigma (photons)
iraf.apall.setParam('gain',4.) # Photon gain (photons/data number)
iraf.apall.setParam('lsigma',4.) # Lower rejection threshold
iraf.apall.setParam('usigma',4.) # Upper rejection threshold
iraf.apall.setParam('nsubaps',1) # Number of subapertures per aperture
iraf.apall()
hdulist = fits.open(os.path.join(targetdir, 'imcomb+bkgd.ms.fits'))
sigma = hdulist[0].data[3,0,:]
hdulist.close()
hdulist = fits.open(os.path.join(targetdir, 'imcomb.ms.fits'), mode='update')
hdulist[0].data[2,0,:] = sigma
hdulist.flush()
hdulist.close()
return None
from numpy import * from scipy.optimize import leastsq,fsolve#,fmin_slsqp from scipy.integrate import quad from scipy.stats import * from datetime import datetime, date, time import matplotlib.pyplot as plt from datetime import timedelta from numpy.random import randint import pyfits iraf.tables() iraf.noao() iraf.imred() iraf.twodspec() iraf.onedspec() iraf.ccdred() iraf.apextract() iraf.longslit() iraf.plot() iraf.stsdas() iraf.nebular() from time import strftime ############################################################# ########Setup a list of lines and fitting regions ############################################################# #the list of lines linenames=[3727,4102,4340,4363,4686,4861,4959,5007,5755,5876,6548,6562,6584,6717,6731] elines=array([.5*(3727.092+3729.875),4102.89,4341.68,4364.436,4686,4862.68,4960.295,5008.240,5754.59,5876,6549.86,6564.61,6585.27,6718.29,6732.67]) #define a preferred background region width
def load_modules():
# Define a function to load all of the modules so that they don't' import
# unless we need them
global iraf
from pyraf import iraf
iraf.pysalt()
iraf.saltspec()
iraf.saltred()
iraf.set(clobber='YES')
global sys
import sys
global os
import os
global shutil
import shutil
global glob
from glob import glob
global pyfits
import pyfits
global np
import numpy as np
global lacosmicx
import lacosmicx
global interp
from scipy import interp
global signal
from scipy import signal
global ndimage
from scipy import ndimage
global interpolate
from scipy import interpolate
global WCS
from astropy.wcs import WCS
global optimize
from scipy import optimize
global ds9
import ds9
global GaussianProcess
from sklearn.gaussian_process import GaussianProcess
global pandas
import pandas
iraf.onedspec()
iraf.twodspec()
iraf.longslit()
iraf.apextract()
iraf.imutil()
def main():
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.crutil()
iraf.twodspec()
iraf.apextract()
name = os.popen('ls object*.lst').readlines()
name = [i.split('\n')[0] for i in name]
if len(name) == 0:
print 'no object, the script end'
for i in name:
print 'open file ' + i + '...'
f = open(i)
l = f.readlines()
f.close()
l = [i2.split('\n')[0] for i2 in l]
for i2 in l:
print i2
objs = []
calibs = []
standards = []
fno = l.index('object') + 1
while fno < len(l):
if '.fits' not in l[fno]:
break
objs.append('ftbo' + l[fno])
fno += 1
fno = l.index('calibration') + 1
while fno < len(l):
if '.fits' not in l[fno]:
break
calibs.append('ftbo' + l[fno])
fno += 1
fno = l.index('standard') + 1
while fno < len(l):
if '.fits' not in l[fno]:
break
standards.append('ftbo' + l[fno])
fno += 1
print '=' * 20, 'reject cosmic ray', '=' * 20
#cobj = shift_align(objs)
#cstandard = shift_align(standards)
cobjs = []
cstandards = []
for eobj in objs:
cobjs.append(cos_clear([eobj]))
for eobj in standards:
cstandards.append(cos_clear([eobj]))
print 'The fits having rejected cosmic ray is: '
print cobjs
print cstandards
print '=' * 20, 'get spectrum line', '=' * 20
acobjs = []
acstandards = []
for cobj in cobjs:
acobjs.append(get_spec_line(cobj))
for cobj in cstandards:
acstandards.append(get_spec_line(cobj))
acalibs = []
for i3 in calibs:
acalibs.append(get_cal_spec_line(i3, cobj))
print '=' * 20, 'wavelength identify', '=' * 20
iacalibs = wl_identy(acalibs)
print '=' * 20, 'wavelength calibrate', '=' * 20
inamepart = i.replace('.','_').split('_')
objnames = []
stdobjnames = []
for numb in xrange(len(acobjs)):
objnames.append( 'iac' + inamepart[1] + '_' + inamepart[2] + '_' + inamepart[3] +
'_' + str(numb) + '.fits')
for numb in xrange(len(acstandards)):
stdobjnames.append('iacstd' + inamepart[1] + '_' + inamepart[2] + '_' + inamepart[3] +
'_' + str(numb) + '.fits')
for numb in xrange(len(objnames)):
wl_cal(acobjs[numb], objnames[numb], iacalibs)
for numb in xrange(len(stdobjnames)):
wl_cal(acstandards[numb], stdobjnames[numb], iacalibs)
print '=' * 20, 'flux calibrate', '=' * 20
for objname in objnames:
final_objname = flux_cal_new(objname, stdobjnames)
print 'the final object name is', final_objname
for j in range(len(file_list)):
if sensfile not in file_list[j]:
if arcfile not in file_list[j]:
if '.ms.fits' not in file_list[j]:
if '.w.fits' not in file_list[j]:
final.append(file_list[j])
### Define the object name
obj = name(final[0])
### Save the list of spectra
np.savetxt('./speclist', final, fmt="%s")
### apall on arc
iraf.twodspec()
iraf.apextract(dispaxis=1)
if extract_arc != 'n':
print('\n' + 'Extracting arc file')
iraf.apall(input=arcfile,
output='arc.ms',
apertur=1,
interac='yes',
find='yes',
nfind=1,
recente='yes',
resize='yes',
background='none')
## identify lines
print('\n' + 'Identify arc lines')
iraf.noao(_doprint=0,Stdout="/dev/null")
iraf.imred(_doprint=0,Stdout="/dev/null")
iraf.ccdred(_doprint=0,Stdout="/dev/null")
# <codecell>
#bias subtract
iraf.ccdproc(images=flat, ccdtype='', fixpix='no', oversca='no', trim='no', zerocor='yes', darkcor='no', flatcor='no', zero='masterbias',Stdout="/dev/null")
# <codecell>
#apall on a flat field
print " + Extract masterflat"
iraf.noao(_doprint=0,Stdout="/dev/null")
iraf.twodspec(_doprint=0,Stdout="/dev/null")
iraf.apextract(_doprint=0,Stdout="/dev/null")
# <codecell>
#adds DISPAXIS=1 to the header
iraf.hedit(images=flat, fields='DISPAXIS', value=1, add='yes', verify='no', update='yes',Stdout="/dev/null")
# <codecell>
iraf.unlearn('apall') #restores initial paramenters to apall
# <codecell>
shift_master(ap_ref[ccd], "database/ap%s" % ap_ref[ccd][:-5])
# <codecell>
def oned_extract(object_b_fn, object_b_fn_ec, order_def, colour):
# Import IRAF modules:
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.apextract(_doprint=0)
# Check input file and reference extraction exist before proceeding:
if os.path.isfile(object_b_fn) == True:
if os.path.isfile(order_def) == True:
# Extract one dimensional spectrum from bias- and background scatter-subtracted
# normalized flat-fielded red object frame using IRAF task apall:
if colour == 'red':
parList = "r_oned_extraction_apall.par"
if os.path.isfile(parList) == True:
iraf.apall.setParList(ParList = "r_oned_extraction_apall.par")
iraf.apall(input=object_b_fn, output=object_b_fn_ec, references=order_def)
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'One dimensional spectral extraction of bias- and '
print 'background-subtracted, normalized flat-fielded '
print 'red object ' + object_b_fn
print 'to create ' + object_b_fn_ec + '.'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'One dimensional extraction IRAF .par file '
print str(parList)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
# Extract one dimensional spectrum from bias- and background scatter-subtracted
# normalized flat-fielded blue object frame using IRAF task apall:
if colour == 'blue':
parList = "b_oned_extraction_apall.par"
if os.path.isfile(parList) == True:
iraf.apall.setParList(ParList = "b_oned_extraction_apall.par")
iraf.apall(input=object_b_fn, output=object_b_fn_ec, references=order_def)
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'One dimensional spectral extraction of bias- and '
print 'background-subtracted, normalized flat-fielded '
print 'blue object ' + object_b_fn
print 'to create ' + object_b_fn_s + '.'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'One dimensional extraction IRAF .par file '
print str(parList)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Order definition frame '
print str(order_def)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Input frame '
print str(object_b_fn)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
lowerclip_gr7 = 3500
upperclip_gr7 = 7720
lowerclip_gr8 = 5900
upperclip_gr8 = 9150
combclip_gr7 = 7065
combclip_gr8 = 6965
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Load IRAF Packages
# ------------------------------------------------------------------------------------------------------------------- #
iraf.noao(_doprint=0)
iraf.images(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.apextract(_doprint=0)
iraf.onedspec(_doprint=0)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Functions For File Handling
# ------------------------------------------------------------------------------------------------------------------- #
def remove_file(file_name):
"""
Removes the file "file_name" in the constituent directory.
Args:
file_name : Name of the file to be removed from the current directory
Returns:
None
# Delete some directories/files from previous runs.
os.system("rm -rf login.cl pyraf database uparm")
os.system("mkiraf")
# Now load IRAF
import pyraf.iraf as iraf
# Load the packages we might need.
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.apextract(_doprint=0)
iraf.unlearn(iraf.apall)
# The name of the science file.
filename = 'vega_9.3narrow.fit'
extracted_filename = 'vega_9.3narrow.ms.fits'
calibrated_filename = 'vega_9.3narrow.calib.fits'
# Delete previous results.
os.system("rm "+extracted_filename+" "+calibrated_filename)
# Make sure that the dispersion axis is in the header.
iraf.hedit(images=[filename], fields=["DISPAXIS"], value=["1"], add="Yes")
def apall(imlist_name, reference=''):
"""
Extract 1d spectrum by tracing column of 2d data.
Interactive mode is turned on by default.
On the interactive mode;
(1) Aperture selection
:w -> e1, e2 : Zoom in
:w -> a : Zoom out
:m : Select aperture
:l -> Click left of the aper. : Set lower limit of the aperture.
:u -> Click right of the aper. : Set upper limit of the aperture.
(2) Background selection
:z : Delete previously-selected background region.
:b : Enter the background mode
:s1, s2 (on the left part of bkg) : Set the region of left bkg.
:s3, s4 (on the right part of bkg) : Set the region of right bkg.
And then enter q --> Yes, Yes
(3) Fitting mode
:d : Exclude a point
:function : Change the fitting function. ("legendre" by default)
:order=15 : Change the function order into 15.
:niterate : Cliping iteration (3 times by default)
:f : Go fitting
:q : Confirm
And the enter q --> Yes, Yes
Output image will be saved as "XXX.ms.fits"
reference='' : Not using specified aperture used.
reference='??.ms' : Use the same aperture of ??.ms. Do copy of database of the reference into the same location of input image***
"""
import glob
import os, sys
from pyraf import iraf
iraf.noao()
iraf.twodspec()
iraf.apextract()
imlist = glob.glob(imlist_name)
imlist.sort()
for i in range(len(imlist)):
inim = imlist[i]
print('1d extraction for ' + inim + '...')
if reference == '':
iraf.apall(input=inim,
apertures='',
interactive='yes',
find='no',
t_function="legendre",
t_order=15,
t_niterate=3,
t_low_reject=3,
t_high_reject=3.)
elif reference != '':
iraf.apall(input=inim,
apertures='',
reference=reference,
interactive='no',
find='no',
recenter='no',
trace='no',
t_function="legendre",
t_order=15,
t_niterate=3,
t_low_reject=3,
t_high_reject=3.)
print('Done.')