def lacosmic(name, irafhome, la_iter):
import time
iraf.task(lacos_spec=irafhome + "lacos_spec.cl")
outname = "la" + name
pl = "mask" + name
iraf.lacos_spec(
input=name,
output=outname,
outmask=pl,
gain=1.0,
readn=2.89,
xorder=9,
yorder=0,
sigclip=4.5,
sigfrac=0.5,
objlim=1.0,
niter=la_iter,
verbose="yes",
mode="al",
)
old = time.time()
pidds9 = subprocess.Popen(["ds9", pl, outname, "-zscale"]).pid
os.system("mv %s history/" % (name))
return pidds9
def test_division(tmpdir):
outfile = str(tmpdir.join('output.txt'))
# First show straight Python
case1 = 9 / 5
case2 = 9 / 5.
case3 = 9 // 5
case4 = 9 // 5.
iraf.printf('a: 9/5 -> ' + str(case1) + '\n', StdoutAppend=outfile)
iraf.printf('b: 9/5. -> ' + str(case2) + '\n', StdoutAppend=outfile)
iraf.printf('c: 9//5 -> ' + str(case3) + '\n', StdoutAppend=outfile)
iraf.printf('d: 9//5. -> ' + str(case4) + '\n', StdoutAppend=outfile)
iraf.printf('\n', StdoutAppend=outfile)
# Then show how a .cl script would be run
iraf.task(xyz='print "e: " (9/5)\nprint "f: " (9/5.)\n'
'print "g: " (9//5)\nprint "h: " (9//5.)', IsCmdString=1)
iraf.xyz(StdoutAppend=outfile)
iraf.printf('\n', StdoutAppend=outfile)
# Then show how pyraf executes command-line instructions via cl2py
iraf.clExecute('print "i: " (9/5)', StdoutAppend=outfile)
iraf.clExecute('print "j: " (9/5.)', StdoutAppend=outfile)
iraf.clExecute('print "k: " (9//5)', StdoutAppend=outfile)
iraf.clExecute('print "l: " (9//5.)', StdoutAppend=outfile)
# Quick step to strip whitespace from lines in output.
# Much easier this way than messing with ancient comparator code.
with open(outfile) as f_in:
stripped = [l.replace(' ', ' ').replace(' ', ' ').strip()
for l in f_in.readlines()]
diff_outputs(stripped, 'data/cli_div_output.ref')
def lacosmic(name,irafhome):
import time
iraf.task(lacos_spec=irafhome+'lacos_spec.cl')
outname = 'la'+name
pl = 'mask'+name
iraf.lacos_spec(input=name,output=outname,outmask=pl,gain=1.,readn=2.89,
xorder=9,yorder=0,sigclip=4.5,sigfrac=0.5,objlim=1.,niter=7,verbose='yes',mode='al')
old = time.time()
os.system('ds9 %s -zscale %s -zscale -blink &' % (pl,name))
os.system('ds9 %s -zscale &' % outname)
return
def define_lacosmic(path_to_lacos_im=''):
"""Moves to ``STSDAS`` package and defines ``LACOS_IM`` as an
``iraf`` task.
Parameters
-----------
path_to_lacos_im : string
Your path to ``lacos_im.cl``.
If left blank, assume Current Working Directory.
#By default, reads from lacos_setup
Notes
-----
The IRAF way is
task lacos_im = /path/lacos_im.cl
"""
iraf.stsdas()
iraf.task(lacos_im=path_to_lacos_im + 'lacos_im.cl')
def lacosmic(name, irafhome, la_iter):
import time
iraf.task(lacos_spec=irafhome + 'lacos_spec.cl')
outname = 'la' + name
pl = 'mask' + name
iraf.lacos_spec(input=name,
output=outname,
outmask=pl,
gain=1.,
readn=2.89,
xorder=9,
yorder=0,
sigclip=4.5,
sigfrac=0.5,
objlim=1.,
niter=la_iter,
verbose='yes',
mode='al')
old = time.time()
pidds9 = subprocess.Popen(['ds9', pl, outname, '-zscale']).pid
os.system('mv %s history/' % (name))
return pidds9
def test_division(tmpdir):
outfile = str(tmpdir.join('output.txt'))
# First show straight Python
case1 = 9 / 5
case2 = 9 / 5.
case3 = 9 // 5
case4 = 9 // 5.
iraf.printf('a: 9/5 -> ' + str(case1) + '\n', StdoutAppend=outfile)
iraf.printf('b: 9/5. -> ' + str(case2) + '\n', StdoutAppend=outfile)
iraf.printf('c: 9//5 -> ' + str(case3) + '\n', StdoutAppend=outfile)
iraf.printf('d: 9//5. -> ' + str(case4) + '\n', StdoutAppend=outfile)
iraf.printf('\n', StdoutAppend=outfile)
# Then show how a .cl script would be run
iraf.task(xyz='print "e: " (9/5)\nprint "f: " (9/5.)\n'
'print "g: " (9//5)\nprint "h: " (9//5.)',
IsCmdString=1)
iraf.xyz(StdoutAppend=outfile)
iraf.printf('\n', StdoutAppend=outfile)
# Then show how pyraf executes command-line instructions via cl2py
iraf.clExecute('print "i: " (9/5)', StdoutAppend=outfile)
iraf.clExecute('print "j: " (9/5.)', StdoutAppend=outfile)
iraf.clExecute('print "k: " (9//5)', StdoutAppend=outfile)
iraf.clExecute('print "l: " (9//5.)', StdoutAppend=outfile)
# Quick step to strip whitespace from lines in output.
# Much easier this way than messing with ancient comparator code.
with open(outfile) as f_in:
stripped = [
l.replace(' ', ' ').replace(' ', ' ').strip()
for l in f_in.readlines()
]
diff_outputs(stripped, 'data/cli_div_output.ref')
from pyraf import iraf
import os
import glob
import asciitable
import numpy as np
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
#cargo paquetes
iraf.noao.rv()
iraf.task(fn2="find2c.cl")
iraf.task(spbina="spbina.cl")
iraf.task(scale="scale.cl")
iraf.noao.onedspec()
iraf.noao.imred()
iraf.noao.ccdred()
iraf.noao.echelle()
def f2c(res_tem="S", step_tem=100): # es el programa en si, todo en iraf
#valores para los template
if res_tem == "h" or res_tem == "H":
lit = "@template"
print("Working with template spectra in hihg resolution")
else:
lit = "@template"
print("Working with template spectra in low resolution")
#parametros de entrada
lio = "@objetos"
lit = "@template"
np.loadtxt('shift.refimage', usecols=(0, ), dtype=str, unpack=True))
# refImageSh = refImageShin[0]
print "reference image: ", refImageSh
filesSh = glob.glob('msc_*.fits')
filesSh.pop(filesSh.index(refImageSh))
filesSh.append(refImageSh)
filesSh.reverse()
if not os.path.isfile('mscFilesSh.list'):
with open('mscFilesSh.list', 'w+') as f1:
for i, mfile in enumerate(filesSh):
print >> f1, mfile
# import the getshfts task as a pyraf task
iraf.task(getshfts="home$scripts/getshfts.cl")
# then run getshfts
if not os.path.isfile('mscFilesSh.shft'):
iraf.getshfts(images='@mscFilesSh.list', rootname='mscFilesSh')
if not os.path.isfile('sh' + refImageSh):
iraf.immatch.imalign.setParam('input', '@mscFilesSh.list')
iraf.immatch.imalign.setParam('reference', refImageSh)
iraf.immatch.imalign.setParam('coords', 'mscFilesSh.reg')
iraf.immatch.imalign.setParam('output', 'sh//@mscFilesSh.list')
iraf.immatch.imalign.setParam('shifts', 'mscFilesSh.shft')
iraf.immatch.imalign.setParam('niterate', 10)
iraf.immatch.imalign.setParam('interp_type', 'poly5')
iraf.immatch.imalign.setParam('boundary_type', 'constant')
iraf.immatch.imalign.setParam('constant', 0.0)
angle_rot_2 = hdu_aux[0].header['HIERARCH ESO INS RETA2 ROT']
if angle_rot == angle_rot_2:
out = wave_extraction(wave,im)
w_image_ms.write(out + '\n')
identify(out,ref_ID)
w_image_ms.seek(0)
break
image_ms.close()
w_image_ms.close()
wave_list.close()
return 'w.ms.' + list_image
if __name__ == '__main__':
iraf.task(lacos_spec= "lacos_spec.cl") #LACOSMIC
abspath = os.path.abspath(__file__) #Definir main_directory
dictionary, path = inputs(sys.argv) #definir inputs
#Setting variables and lacos_spec in directory
wave = dictionary['wave']
flat = dictionary['flat']
std = dictionary['std']
science = dictionary['science']
flux = dictionary['flux']
bias = dictionary['bias']
#COSMIC REMOVAL
c_wave = 'c' + wave
c_flat = 'c' + flat
c_science = 'c' + science
c_flux = 'c' + flux
#c_std = 'c' + std
else:
_iraf = ns._home + "/atwork/iraf/"
# Eventually, get all initializations from initobs:
print data
obs = ns.initobs(data, remote=(not local))
_proc = obs[1]
_raw = obs[8]
n_ap = obs[14] # number of apertures (i.e., echelle orders)
filter = obs[15] # photometric band in which we're operating
prefn = obs[16] # filename prefix
calnod = obs[17] # whether A0V calibrators nod, or not
procData = processCal or processTarg
badval = 0
ir.task(bfixpix=_iraf + "bfixpix.cl")
ir.task(bfixpix_one=_iraf + "bfixpix_one.cl")
#ir.load('fitsutil')
ir.load('noao')
ir.load('astutil')
ir.load("imred")
ir.load('echelle')
ir.load('twodspec')
ir.load('apextract')
telluric_list = ns._home + '/proj/pcsa/data/atmo/telluric_hr_' + filter + '.dat'
if filter == 'K' or filter == 'H':
horizsamp = "10:500 550:995"
elif filter == 'L':
horizsamp = "10:270 440:500 550:980"
def reduce_science(rawdir, rundir, flat, arc, twilight, twilight_flat, sciimg,
starimg, bias, overscan, vardq, observatory, lacos,
apply_lacos, lacos_xorder, lacos_yorder, lacos_sigclip,
lacos_objlim, bpm, instrument, slits, fl_gscrrej,
wltrim_frac, grow_gap, cube_bit_mask):
"""
Reduction pipeline for standard star.
Parameters
----------
rawdir: string
Directory containing raw images.
rundir: string
Directory where processed files are saved.
flat: string
Names of the files containing flat field images.
arc: string
Arc images.
twilight: string
Twilight flat images.
twilight_flat: string
Flat field for twilight image.
starimg: string
Name of the file containing the image to be reduced.
bias: string
Bias images.
grow_gap: number
Number of pixels by which to grow the bad pixel mask around
the chip gaps.
"""
iraf.set(stdimage='imtgmos')
iraf.gemini()
iraf.unlearn('gemini')
iraf.gmos()
iraf.unlearn('gmos')
iraf.gemtools()
iraf.unlearn('gemtools')
# os.path.isfile('arquivo')
iraf.unlearn('gemini')
iraf.unlearn('gmos')
iraf.task(lacos_spec=lacos)
# set directories
iraf.set(caldir=rawdir) #
iraf.set(rawdir=rawdir) # raw files
iraf.set(procdir=rundir) # processed files
iraf.gmos.logfile = 'logfile.log'
iraf.gfextract.verbose = 'no'
iraf.cd('procdir')
flat = flat.replace('.fits', '')
twilight = twilight.replace('.fits', '')
twilight_flat = twilight_flat.replace('.fits', '')
arc = arc.replace('.fits', '')
starimg = starimg.replace('.fits', '')
sciimg = sciimg.replace('.fits', '')
mdffile = 'mdf' + flat + '.fits'
iraf.gfreduce.bias = 'caldir$' + bias
iraf.gfreduce.fl_fulldq = 'yes'
iraf.gfreduce.fl_fixgaps = 'yes'
iraf.gfreduce.grow = grow_gap
iraf.gireduce.bpm = 'rawdir$' + bpm
iraf.gfextract.verbose = 'no'
cal_reduction(rawdir=rawdir,
rundir=rundir,
flat=flat,
arc=arc,
twilight=twilight,
bias=bias,
bpm=bpm,
overscan=overscan,
vardq=vardq,
instrument=instrument,
slits=slits,
twilight_flat=twilight_flat,
grow_gap=grow_gap)
#
# Actually reduce science
#
image_name = 'rg' + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gfreduce(sciimg,
slits='header',
rawpath='rawdir$',
fl_inter='no',
fl_addmdf='yes',
key_mdf='MDF',
mdffile=mdffile,
weights='no',
fl_over=overscan,
fl_trim='yes',
fl_bias='yes',
trace='no',
recenter='no',
fl_fulldq='yes',
fl_flux='no',
fl_gscrrej='no',
fl_extract='no',
fl_gsappwave='no',
fl_wavtran='no',
fl_novl='yes',
fl_skysub='no',
fl_vardq=vardq,
mdfdir='procdir$')
prefix = 'rg'
# Gemfix
image_name = 'p' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gemfix(prefix + sciimg,
out='p' + prefix + sciimg,
method='fit1d',
bitmask=65535,
axis=1)
prefix = 'p' + prefix
# LA Cosmic - cosmic ray removal
if apply_lacos:
image_name = 'l' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gemcrspec(prefix + sciimg,
out='l' + prefix + sciimg,
sigfrac=0.5,
niter=4,
fl_vardq=vardq,
xorder=lacos_xorder,
yorder=lacos_yorder,
sigclip=lacos_sigclip,
objlim=lacos_objlim)
prefix = 'l' + prefix
if fl_gscrrej:
image_name = 'ex' + prefix + sciimg + '.fits'
else:
image_name = 'e' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gfreduce(prefix + sciimg,
slits='header',
rawpath='./',
fl_inter='no',
fl_addmdf='no',
key_mdf='MDF',
mdffile=mdffile,
fl_over='no',
fl_trim='no',
fl_bias='no',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej=fl_gscrrej,
fl_extract='yes',
fl_gsappwave='yes',
fl_wavtran='no',
fl_novl='no',
fl_skysub='no',
grow=grow_gap,
reference='eprg' + flat,
weights='no',
wavtraname='eprg' + arc,
response='eprg' + flat + '_response.fits',
fl_vardq=vardq,
fl_fulldq='yes',
fl_fixgaps='yes')
if fl_gscrrej:
prefix = 'ex' + prefix
else:
prefix = 'e' + prefix
# if wl2 > 7550.0:
# wl2 = 7550.0
#
# Apply wavelength transformation
#
wl1, wl2 = wl_lims(prefix + sciimg + '.fits', wltrim_frac)
image_name = 't' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gftransform(
prefix + sciimg,
wavtraname='eprg' + arc,
fl_vardq=vardq,
w1=wl1,
w2=wl2,
)
prefix = 't' + prefix
#
# Sky subtraction
#
image_name = 's' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gfskysub(
prefix + sciimg,
expr='default',
combine='median',
reject='avsigclip',
scale='none',
zero='none',
weight='none',
sepslits='yes',
fl_inter='no',
lsigma=1,
hsigma=1,
)
prefix = 's' + prefix
#
# Apply flux calibration to galaxy
#
image_name = 'c' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gscalibrate(prefix + sciimg,
sfuncti=starimg,
extinct='onedstds$ctioextinct.dat',
observatory=observatory,
fluxsca=1,
fl_vardq=vardq)
prefix = 'c' + prefix
#
# Remove spurious data with PCA
#
image_name = 'x' + prefix + sciimg + '.fits'
print(os.getcwd())
print(image_name)
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
t = pca.Tomography(prefix + sciimg + '.fits')
t.decompose()
t.remove_cosmic_rays(sigma_threshold=10.0)
t.write(image_name)
prefix = 'x' + prefix
#
# Create data cubes
#
image_name = 'd' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
data_cube = CubeBuilder(prefix + sciimg + '.fits')
data_cube.build_cube()
data_cube.fit_refraction_function()
data_cube.fix_atmospheric_refraction()
data_cube.write(image_name)
gain = 1.41
readnoise = 4.64
elif teles == "LJT":
print("Settings for LJT will be used.")
gain = 3.20
readnoise = 13.5
else:
print("Error detected.")
objall = []
with open('objall.list') as file:
for line in file:
line = line.strip('.fits\n')
objall.append(line)
iraf.task(lacos_im=lacos_im)
print('Loading IRAF packages ...')
iraf.stsdas()
iraf.lacos_im.gain = gain
iraf.lacos_im.readn = readnoise
iraf.images()
iraf.images.imutil()
iraf.images.imutil.imheader(images="@objall.list")
stdspecs = str(
raw_input("Enter filenames of all standard star spectra, \n\
separated by comma (','): "))
stdlist = [x.strip('.fits') for x in stdspecs.split(',')]
stdlist = [x.strip() for x in stdlist]
for item in stdlist:
objall.remove(item)
"No pyraf module -- did you forget to do `source activate geminiconda`?"
)
import copy
import glob
import os
import shutil
import argparse
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.table import Table
from pyraf.iraf import gemini, gmos, gemtools, task
# this task allows us to skip bad slits in gswavelenght!
iraf.task(mgswavelength='/Users/mwilde/iraf/mgswavelength.cl')
path = os.path.abspath('.')
# Set colormap for plotting
cm = plt.get_cmap('Greys')
flatPrefix = 'MC' + 'gcalFlat'
combName = 'MC' + 'gcalFlat' + 'Comb'
biasName = 'MCbiasFull.fits'
# Read in user defined slit(s) to skip with mgswavelength
parser = argparse.ArgumentParser(
description="run the iraf reduction on cgmsquared data.")
parser.add_argument("--skip_slit",
type=int,
refImageSh = str(np.loadtxt('shift.refimage', usecols=(0,), dtype=str, unpack=True))
# refImageSh = refImageShin[0]
print "reference image: ", refImageSh
filesSh = glob.glob('msc_*.fits')
filesSh.pop(filesSh.index(refImageSh))
filesSh.append(refImageSh)
filesSh.reverse()
if not os.path.isfile('mscFilesSh.list'):
with open('mscFilesSh.list','w+') as f1:
for i,mfile in enumerate(filesSh):
print >> f1, mfile
# import the getshfts task as a pyraf task
iraf.task(getshfts = "home$scripts/getshfts.cl")
# then run getshfts
if not os.path.isfile('mscFilesSh.shft'):
iraf.getshfts(images='@mscFilesSh.list', rootname='mscFilesSh')
if not os.path.isfile('sh'+refImageSh):
iraf.immatch.imalign.setParam('input','@mscFilesSh.list')
iraf.immatch.imalign.setParam('reference',refImageSh)
iraf.immatch.imalign.setParam('coords','mscFilesSh.reg')
iraf.immatch.imalign.setParam('output','sh//@mscFilesSh.list')
iraf.immatch.imalign.setParam('shifts','mscFilesSh.shft')
iraf.immatch.imalign.setParam('niterate',10)
iraf.immatch.imalign.setParam('interp_type','poly5')
iraf.immatch.imalign.setParam('boundary_type','constant')
iraf.immatch.imalign.setParam('constant',0.0)
# print matched sources to a file suitable for marking
if os.path.isfile('ifirst_tol7.out') :
mx,my = np.loadtxt('ifirst_tol7.out',usecols=(4,5),unpack=True)
mfilter = np.loadtxt('ifirst_tol7.out',usecols=(1,),dtype=str,unpack=True)
match_pos_file_g = open("tol7_g.pos", 'w+')
match_pos_file_i = open("tol7_i.pos", 'w+')
for i in range(len(mx)) :
if mfilter[i]== 'g' :
print >> match_pos_file_g, mx[i], my[i]
if mfilter[i] == 'r' :
print >> match_pos_file_i, mx[i], my[i]
match_pos_file_g.close()
match_pos_file_i.close()
# import the getfwhm task as a pyraf task
iraf.task(getfwhm = "home$scripts/getfwhm.cl")
# print iraf.getfwhm.getCode()
# you might want to remeasure the FWHMs to get a better global estimate now that we (should) only have good sources in the image
# use getfwhm, which is just a loop on imexam. (try to improve this with ralf's qr code)
if not os.path.isfile('getfwhm_g.log') :
iraf.unlearn(iraf.imexamine, iraf.rimexam)
iraf.getfwhm.setParam('images',fits_g)
iraf.getfwhm.setParam('coordlist','tol7_g.pos')
iraf.getfwhm.setParam('outfile','getfwhm_g.log')
iraf.getfwhm.setParam('center','no')
iraf.imexamine.setParam('frame',1)
iraf.getfwhm()
if not os.path.isfile('getfwhm_i.log') :
iraf.getfwhm.setParam('images',fits_i)
def reduce_stdstar(rawdir,
rundir,
caldir,
starobj,
stdstar,
flat,
arc,
twilight,
twilight_flat,
starimg,
bias,
overscan,
vardq,
lacos,
observatory,
apply_lacos,
lacos_xorder,
lacos_yorder,
lacos_objlim,
lacos_sigclip,
bpm,
instrument,
slits,
fl_gscrrej,
wltrim_frac=0.03,
sens_order=6,
sens_function='spline3',
apsum_radius=1):
"""
Reduction pipeline for standard star.
Parameters
----------
rawdir: string
Directory containing raw images.
rundi: string
Directory where processed files are saved.
caldir: string
Directory containing standard star calibration files.
starobj: string
Object keyword for the star image.
stdstar: string
Star name in calibration file.
flat: list
Names of the files containing flat field images.
arc: list
Arc images.
twilight: list
Twilight flat images.
starimg: string
Name of the file containing the image to be reduced.
bias: list
Bias images.
"""
iraf.set(stdimage='imtgmos')
iraf.task(lacos_spec=lacos)
iraf.gemini()
iraf.unlearn('gemini')
iraf.gmos()
iraf.unlearn('gmos')
iraf.gemtools()
iraf.unlearn('gemtools')
iraf.gmos.logfile = 'logfile.log'
iraf.gemtools.gloginit.logfile = 'logfile.log'
iraf.gfextract.verbose = 'no'
# set directories
iraf.set(caldir=rawdir) #
iraf.set(rawdir=rawdir) # raw files
iraf.set(procdir=rundir) # processed files
# os.path.isfile('arquivo')
iraf.cd('procdir')
flat = flat.strip('.fits')
twilight = twilight.strip('.fits')
twilight_flat = twilight_flat.strip('.fits')
arc = arc.strip('.fits')
starimg = starimg.strip('.fits')
mdffile = 'mdf' + flat + '.fits'
iraf.gfreduce.bias = 'rawdir$' + bias
iraf.gfreduce.fl_fulldq = 'yes'
iraf.gfreduce.fl_fixgaps = 'yes'
iraf.gireduce.bpm = 'rawdir$' + bpm
cal_reduction(rawdir=rawdir,
rundir=rundir,
flat=flat,
arc=arc,
twilight=twilight,
bias=bias,
bpm=bpm,
overscan=overscan,
vardq=vardq,
instrument=instrument,
slits=slits,
twilight_flat=twilight_flat)
#
# Actually reduce star
#
imageName = 'rg' + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
imageName = 'g' + starimg + '.fits'
if os.path.isfile(imageName):
iraf.printlog(
'GIREDS: WARNING: Removing file {:s}'.format(imageName),
'logfile.log', 'yes')
iraf.delete(imageName)
iraf.gfreduce(starimg,
slits='header',
rawpath='rawdir$',
fl_inter='no',
fl_addmdf='yes',
key_mdf='MDF',
mdffile=mdffile,
weights='no',
fl_over=overscan,
fl_trim='yes',
fl_bias='yes',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej='no',
fl_extract='no',
fl_gsappwave='no',
fl_wavtran='no',
fl_novl='yes',
fl_skysub='no',
fl_vardq=vardq,
mdfdir='procdir$')
prefix = 'rg'
#
# Gemfix
#
imageName = 'p' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gemfix(prefix + starimg,
out='p' + prefix + starimg,
method='fit1d',
bitmask=1,
axis=1)
prefix = 'p' + prefix
#
# LA Cosmic
#
if apply_lacos:
imageName = 'l' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
if apply_lacos:
iraf.gemcrspec(prefix + starimg,
out='l' + prefix + starimg,
sigfrac=0.32,
niter=4,
fl_vardq=vardq,
xorder=lacos_xorder,
yorder=lacos_yorder,
objlim=lacos_objlim,
sigclip=lacos_sigclip)
prefix = 'l' + prefix
#
# Extraction and Gemini's comsmic ray rejection
#
if fl_gscrrej:
imageName = 'x' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
fl_gscrrej = False
else:
imageName = 'ex' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gfreduce(prefix + starimg,
slits='header',
rawpath='./',
fl_inter='no',
fl_addmdf='no',
key_mdf='MDF',
mdffile=mdffile,
fl_over='no',
fl_trim='no',
fl_bias='no',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej=fl_gscrrej,
fl_extract='yes',
fl_gsappwave='yes',
fl_wavtran='no',
fl_novl='no',
fl_skysub='no',
reference='eprg' + flat,
weights='no',
wavtraname='eprg' + arc,
response='eprg' + flat + '_response.fits',
fl_vardq=vardq)
prefix = 'ex' + prefix
else:
imageName = 'e' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gfreduce(prefix + starimg,
slits='header',
rawpath='./',
fl_inter='no',
fl_addmdf='no',
key_mdf='MDF',
mdffile=mdffile,
fl_over='no',
fl_trim='no',
fl_bias='no',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej=fl_gscrrej,
fl_extract='yes',
fl_gsappwave='yes',
fl_wavtran='no',
fl_novl='no',
fl_skysub='no',
reference='eprg' + flat,
weights='no',
wavtraname='eprg' + arc,
response='eprg' + flat + '_response.fits',
fl_vardq=vardq)
prefix = 'e' + prefix
#
# Wavelength transform
#
wl1, wl2 = wl_lims(prefix + starimg + '.fits', wltrim_frac)
imageName = 't' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gfreduce(prefix + starimg,
slits='header',
rawpath='./',
fl_inter='no',
fl_addmdf='no',
key_mdf='MDF',
mdffile=mdffile,
fl_over='no',
fl_trim='no',
fl_bias='no',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej='no',
fl_extract='no',
fl_gsappwave='no',
fl_wavtran='yes',
fl_novl='no',
fl_skysub='no',
reference='eprg' + flat,
weights='no',
wavtraname='eprg' + arc,
response='eprg' + flat + '_response.fits',
fl_vardq=vardq,
w1=wl1,
w2=wl2)
prefix = 't' + prefix
#
# Sky subtraction
#
imageName = 's' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gfreduce(prefix + starimg,
slits='header',
rawpath='./',
fl_inter='no',
fl_addmdf='no',
key_mdf='MDF',
mdffile=mdffile,
fl_over='no',
fl_trim='no',
fl_bias='no',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej='no',
fl_extract='no',
fl_gsappwave='no',
fl_wavtran='no',
fl_novl='no',
fl_skysub='yes',
reference='eprg' + flat,
weights='no',
wavtraname='eprg' + arc,
response='eprg' + flat + '_response.fits',
fl_vardq=vardq,
w1=wl1,
w2=wl2)
prefix = 's' + prefix
#
# Apsumming the stellar spectra
#
xinst = pf.getdata(prefix + starimg + '.fits', ext=1)['XINST']
if instrument == 'GMOS-N':
x0 = np.average(xinst[xinst < 10])
elif instrument == 'GMOS-S':
x0 = np.average(xinst[xinst > 10])
ap_expression = '((XINST-{:.2f})**2 + '\
'(YINST-2.45)**2)**0.5 < {:.2f}'.format(x0, apsum_radius)
imageName = 'a' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gfapsum(prefix + starimg,
fl_inter='no',
lthreshold='INDEF',
hthreshold='INDEF',
reject='avsigclip',
expr=ap_expression)
#
# Building sensibility function
#
if os.path.isfile('std' + starimg)\
and os.path.isfile('sens' + starimg + '.fits'):
skipwarn('std{0:s} and sens{0:s}.fits'.format(starimg))
else:
imageName = 'std' + starimg
if os.path.isfile(imageName):
iraf.printlog(
'GIREDS: WARNING: Removing file {:s}'.format(imageName),
'logfile.log', 'yes')
iraf.delete(imageName)
imageName = 'sens' + starimg + '.fits'
if os.path.isfile(imageName):
iraf.printlog(
'GIREDS: WARNING: Removing file {:s}'.format(imageName),
'logfile.log', 'yes')
iraf.delete(imageName)
iraf.gsstandard('a' + prefix + starimg,
starname=stdstar,
observatory=observatory,
sfile='std' + starimg,
sfunction='sens' + starimg,
caldir=caldir,
order=sens_order,
function=sens_function)
#
# Apply flux calibration to star
#
imageName = 'c' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gscalibrate(prefix + starimg,
sfuncti='sens' + starimg,
extinct='onedstds$ctioextinct.dat',
observatory=observatory,
fluxsca=1,
fl_vardq=vardq)
#
# Create data cubes
#
imageName = 'dc' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
data_cube = CubeBuilder('c' + prefix + starimg + '.fits')
data_cube.build_cube()
data_cube.fit_refraction_function()
data_cube.fix_atmospheric_refraction()
data_cube.write(imageName)
#
# Test calibration
#
iraf.cd(caldir)
caldata = np.loadtxt(stdstar + '.dat', unpack=True)
iraf.cd('procdir')
calflux = mag2flux(caldata[0], caldata[1])
imageName = 'ca' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gscalibrate('a' + prefix + starimg,
sfuncti='sens' + starimg,
extinct='onedstds$ctioextinct.dat',
observatory=observatory,
fluxsca=1)
sumflux = pf.getdata('ca' + prefix + starimg + '.fits', ext=2)
sumhead = pf.getheader('ca' + prefix + starimg + '.fits', ext=2)
sumwl = sumhead['crval1'] + np.arange(
sumhead['naxis1']) * sumhead['cdelt1']
plt.close('all')
plt.plot(sumwl, sumflux, 'b', lw=.5)
plt.plot(caldata[0], calflux, 'r', lw=1.5)
plt.xlim(sumwl[0] * .99, sumwl[-1] * 1.01)
plt.ylim(min(calflux) * .8, max(calflux) * 1.2)
plt.savefig('calib' + starimg + '.eps')
Returns:
--------
Table with the distorted X,Y coordinates for all the stars
"""
import os
import sys
import glob
from pyraf import iraf
#Getting access to Iraf
login = "******"
iraf.task(login = "******")
p = iraf.login.getCode()
eval('p')
def Geoxy(path):
print "Starting Geoxy..."
filename = path
os.chdir(filename)
corrected = glob.glob("*.xy")
#Removing existing files
filelist = glob.glob("*.done")
for f in filelist:
os.remove(f)
from astropy.io import fits
from matplotlib import pyplot as plt
import shutil
import sys
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.kpnoslit(_doprint=0)
iraf.astutil(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.twodspec.longslit.dispaxis = 2
iraf.module.load('stsdas', doprint=0, hush=1)
iraf.task(lacos_spec=irafhome + "lacos_spec.cl")
#read object keywords
for file in os.listdir(os.getcwd()):
if file.endswith('.fits'):
testfile = file
shutil.copy(file, './original/.')
hduo = fits.open(testfile)
#name targets (science & standard)
target = hduo[0].header['OBJECT']
#target2 = 'SP0644p375'
#std = 'SP0305+261'
#create list of science files
from pyraf import iraf iraf.task(asgred = "home$asgred/asgred.cl") iraf.task(copred = "home$copred/copred.cl")
iraf.set(procdir='/home/eslamba/ic/reducao/NGC5728/procSTAR_15-01-16/') # processed files
locrep = '/home/eslamba/Dropbox/ic/reducao/15-01-16/ctionewcal_flux/' # star flux files
iraf.cd('rawdir')
iraf.set(stdimage='imtgmos')
iraf.gemini()
iraf.gemtools()
iraf.gmos()
iraf.unlearn('gemini')
iraf.unlearn('gmos')
iraf.gmos.logfile='ltt7379.log'
iraf.task(lacos_spec='/home/eslamba/LAcosmic/lacos_spec.cl')
tstart = time.time()
#iraf.setjd('*fits[0]')
l = glob.glob('*.fits')
l.sort()
idx = np.arange(len(l))
headers = [pf.getheader(i, ext=0) for i in l]
jd = [pf.getheader(i, ext=1)['mjd-obs'] for i in l]
# Define lists
import re #importing regular expressions -- for a logfile issue later
import IPython
import sys
import os
import pyfits
import pyraf
from pyraf import iraf
import numpy as np
os.system('rm *.fits_cursor_file.dat')
os.system('rm *_continuum_fit')
os.system('rm *_continuum_fit_yoffset.dat')
os.system('rm *_scaling_factors.dat')
iraf.task(gscombine='/net/frigg/Users/inger/Gemini/Pipeline/Software/gmos/gscombine.cl')
iraf.unlearn('imexamine')
iraf.unlearn('kimexam') #this is the task I'm actually using when I call the 'k' keyword in imexamine
iraf.unlearn('gscombine')
#I was thinking of using INSTRES as way to code getting the line strengths and comparing them but after tinkering around and not getting too far (bc I couldn't get it to fit the lines I wanted) I figure I'll give imexamine a try. It'll allow me to us wavelength space instead of pixel space and I can use various tools on it; and I know how to code it without being interactive. But wait, I need to fit a gaussian to it so I can get the peak and width to calculate the area underneath. I can use the 'a' function with imexamine to get the peak and then use that as input for jimexamine with fits with a gaussian. Even though it is in pixel space it shouldn't make a difference if I'm comparing the same slits of different science frames of the same instrument setup? I can use the non-interactive version of imexamine to fit a gaussian to the peak line and have the output of the gaussian saved in a log file; I can read the log file and compute the area of the gaussian based on the peak and fwhm values -- I can script this for the other files of the same setup and compare their gaussian area values and therefore calculate their weight
#this is obsolete now since I'm no longer sampling emission lines but sampling a continuum area and I've specified the wavelengths already
def wavelength_to_pixel(fits, sample_ext, sample_wl):
slit_header = pyfits.getheader(fits, sample_ext)
crpix1 = slit_header['CRPIX1']
cd1_1 = slit_header['CD1_1']
crval1 = slit_header['CRVAL1']
sample_pixel = crpix1 + ((sample_wl - crval1) / cd1_1)
def reduce_stdstar(rawdir, rundir, caldir, starobj, stdstar, flat,
arc, twilight, starimg, bias, overscan, vardq):
"""
Reduction pipeline for standard star.
Parameters
----------
rawdir: string
Directory containing raw images.
rundi: string
Directory where processed files are saved.
caldir: string
Directory containing standard star calibration files.
starobj: string
Object keyword for the star image.
stdstar: string
Star name in calibration file.
flat: list
Names of the files containing flat field images.
arc: list
Arc images.
twilight: list
Twilight flat images.
starimg: string
Name of the file containing the image to be reduced.
bias: list
Bias images.
"""
iraf.set(stdimage='imtgmos')
iraf.gemini()
iraf.gemtools()
iraf.gmos()
#iraf.unlearn('gemini')
#iraf.unlearn('gmos')
iraf.task(lacos_spec='/storage/work/gemini_pairs/lacos_spec.cl')
tstart = time.time()
#set directories
iraf.set(caldir=rawdir) #
iraf.set(rawdir=rawdir) # raw files
iraf.set(procdir=rundir) # processed files
iraf.gmos.logfile='logfile.log'
iraf.cd('procdir')
# building lists
def range_string(l):
return (len(l)*'{:4s},').format(*[i[-9:-5] for i in l])
iraf.gemlist(range=range_string(flat), root=flat[0][:-9],
Stdout='flat.list')
iraf.gemlist(range=range_string(arc), root=arc[0][:-9],
Stdout='arc.list')
#iraf.gemlist(range=range_string(star), root=star[0][:-4],
# Stdout='star.list')
iraf.gemlist(range=range_string(twilight),
root=twilight[0][:-9], Stdout='twilight.list')
iraf.gfreduce.bias = 'caldir$'+bias[0]
#######################################################################
#######################################################################
### Star reduction #
#######################################################################
#######################################################################
#
# Flat reduction
#
iraf.gfreduce(
'@flat.list', slits='header', rawpath='rawdir$', fl_inter='no',
fl_addmdf='yes', key_mdf='MDF', mdffile='default', weights='no',
fl_over=overscan, fl_trim='yes', fl_bias='yes', trace='yes', t_order=4,
fl_flux='no', fl_gscrrej='no', fl_extract='yes', fl_gsappwave='no',
fl_wavtran='no', fl_novl='no', fl_skysub='no', reference='',
recenter='yes', fl_vardq=vardq)
iraf.gfreduce('@twilight.list', slits='header', rawpath='rawdir$',
fl_inter='no', fl_addmdf='yes', key_mdf='MDF',
mdffile='default', weights='no',
fl_over=overscan, fl_trim='yes', fl_bias='yes', trace='yes',
recenter='no',
fl_flux='no', fl_gscrrej='no', fl_extract='yes', fl_gsappwave='no',
fl_wavtran='no', fl_novl='no', fl_skysub='no',
reference='erg'+flat[0], fl_vardq=vardq)
#
# Response function
#
for i, j in enumerate(flat):
j = j[:-5]
iraf.imdelete(j+'_response')
iraf.gfresponse('erg'+j+'.fits', out='erg'+j+'_response',
skyimage='erg'+twilight[i], order=95, fl_inter='no',
func='spline3',
sample='*', verbose='yes')
# Arc reduction
#
iraf.gfreduce(
'@arc.list', slits='header', rawpath='rawdir$', fl_inter='no',
fl_addmdf='yes', key_mdf='MDF', mdffile='default', weights='no',
fl_over=overscan, fl_trim='yes', fl_bias='yes', trace='no',
recenter='no',
fl_flux='no', fl_gscrrej='no', fl_extract='yes', fl_gsappwave='no',
fl_wavtran='no', fl_novl='no', fl_skysub='no', reference='erg'+flat[0],
fl_vardq=vardq)
# Finding wavelength solution
# Note: the automatic identification is very good
#
for i in arc:
iraf.gswavelength('erg'+i, function='chebyshev', nsum=15, order=4,
fl_inter='no', nlost=5, ntarget=20, aiddebug='s', threshold=5,
section='middle line')
#
# Apply wavelength solution to the lamp 2D spectra
#
iraf.gftransform('erg'+i, wavtran='erg'+i, outpref='t', fl_vardq=vardq)
##
## Actually reduce star
##
iraf.gfreduce(
starimg, slits='header', rawpath='rawdir$', fl_inter='no',
fl_addmdf='yes', key_mdf='MDF', mdffile='default', weights='no',
fl_over=overscan, fl_trim='yes', fl_bias='yes', trace='no',
recenter='no',
fl_flux='no', fl_gscrrej='no', fl_extract='no', fl_gsappwave='no',
fl_wavtran='no', fl_novl='yes', fl_skysub='no', fl_vardq=vardq)
iraf.gemcrspec('rg{:s}'.format(starimg), out='lrg'+starimg, sigfrac=0.32,
niter=4, fl_vardq=vardq)
iraf.gfreduce(
'lrg'+starimg, slits='header', rawpath='./', fl_inter='no',
fl_addmdf='no', key_mdf='MDF', mdffile='default',
fl_over='no', fl_trim='no', fl_bias='no', trace='no',
recenter='no',
fl_flux='no', fl_gscrrej='no', fl_extract='yes',
fl_gsappwave='yes',
fl_wavtran='yes', fl_novl='no', fl_skysub='yes',
reference='erg'+flat[0][:-5], weights='no',
wavtraname='erg'+arc[0][:-5],
response='erg'+flat[0][:-5]+'_response.fits',
fl_vardq=vardq)
#
# Apsumming the stellar spectra
#
iraf.gfapsum(
'stexlrg'+starimg, fl_inter='no', lthreshold=400.,
reject='avsigclip')
#
# Building sensibility function
#
iraf.gsstandard(
('astexlrg{:s}').format(starimg), starname=stdstar,
observatory='Gemini-South', sfile='std', sfunction='sens',
caldir=caldir)
#
# Apply flux calibration to galaxy
#
#
##iraf.imdelete('*****@*****.**')
#
##iraf.gscalibrate('*****@*****.**',sfunction='sens.fits',fl_ext='yes',extinct='onedstds$ctioextinct.dat',observatory='Gemini-South',fluxsca=1)
#
##
## Create data cubes
##
#
#
##for i in objs:
## iraf.imdelete('d0.1cstexlrg'+i+'.fits')
## iraf.gfcube('cstexlrg'+i+'.fits',outpref='d0.1',ssample=0.1,fl_atmd='yes',fl_flux='yes')
#
##
## Combine cubes
##
#
#
##iraf.imdelete('am2306-721r4_wcsoffsets.fits')
##iraf.imcombine('d0.1cstexlrgS20141113S00??.fits[1]',output='am2306-721r4_wcsoffsets.fits',combine='average',reject='sigclip',masktype='badvalue',lsigma=2,hsigma=2,offset='wcs',outlimits='2 67 2 48 100 1795')
#
tend = time.time()
print('Elapsed time in reduction: {:.2f}'.format(tend - tstart))
from pyraf import iraf
import glob
import os
import sys
file_list=glob.glob('IRCA*.fits')
file_list.sort()
###Left
#ch1='[1:256,1:1024]' #o
#ch2='[257:512,1:1024]' #e
###Right
ch1='[512:768,1:1024]'
ch2='[768:1024,1:1024]'
iraf.task(distcor = 'distcor.cl')
geomap='ircs+ao188_20mas_distmap_20131118.dbs'
flat='calflat.fits'
if os.path.isfile(flat):
print('Using {}'.format(flat))
else:
print('flat image not found!')
sys.exit()
for filename in file_list:
#flatfielding
iraf.imar(filename,'/',flat,filename[:-5]+'f.fits')
#geometric distortion correction
iraf.distcor(filename,filename[:-5]+'fg.fits',geomap)
a1=filename[:-5]+ch1
except:
import pyfits as pf
import logging
file_list = glob.glob('IRCA*.fits')
file_list.sort()
#logger
fname = pf.open(file_list[0])[0].header['FRAMEID'] + '_reduction.log'
logging.basicConfig(filename=fname, level=logging.INFO)
ch1 = '[1:256,1:1024]' #o
ch2 = '[257:512,1:1024]' #e
distcorr_file = 'distcor.cl'
iraf.task(distcor=distcorr_file)
geomap = 'ircs+ao188_20mas_distmap_20131118.dbs'
flat = 'calflat.fits'
#dark_folder = 'data/imaging/ircs_UH30B/CALIB/DARK'
dark_dir = '../CALIB/DARK/master_darks'
if not os.path.exists(dark_dir):
print('Dark folder does is empty: {}'.format(dark_folder))
logging.warning('dark file does not exist')
sys.exit()
for filename in file_list:
#get header
hdr = pf.open(filename)[0].header
#get specific dark
#exptime='master_dark_'+str(hdr['EXP1TIME'])+'s.fits'