def rvsao(bin_sci, task, template_spectra, rvsao_file, rvsao_bin_list, interactive="no", linesig=1.5,
czguess=0., st_lambda="INDEF", end_lambda="INDEF", badlines=None):
"""
Use the rvsao task emsao or xcsao to measure relative velocity from emission or absoption spectra
Note: make sure you select the parameters for your desired use. Refer to the IRAF rvsao package help for description
of what each parameter is/does
"""
assert task == 'xcsao' or task == 'emsao', "task is not either 'xcsao' or 'emsao'"
if os.path.exists(rvsao_file):
print('File {} already exists'.format(rvsao_file))
return
fixbad = "no"
if badlines:
fixbad = "yes"
bin_list = []
for i in range(len(glob.glob(bin_sci.format('*')))): # to ensure order is 0-61 (not 0, 1, 10, 11, etc)
bin_list.append(bin_sci.format(i))
assert len(bin_list) > 0, 'Absorption/emission(?) bin spectra do not exist: {}'.format(em_bin_sci.format('*'))
np.array(bin_list).tofile(rvsao_bin_list, sep='\n')
from pyraf import iraf
iraf.images()
iraf.rvsao()
if task == 'xcsao':
iraf.xcsao('@{}'.format(rvsao_bin_list), templates=bin_sci.format(template_spectra), report_mode=2,
logfiles=rvsao_file, displot=interactive, low_bin=10, top_low=20, top_nrun=80, nrun=211,
zeropad="yes", nzpass=1, curmode="no", pkmode=2, s_emchop="no", vel_init="guess", czguess=czguess,
st_lambda=st_lambda, end_lambda=end_lambda, fixbad=fixbad, badlines=badlines)
elif task == 'emsao':
# Run this interactively as the fit can fail often. Depending on S/N you may have to decrease the linesig
# to detect the lines, or if lines are matched inconsistently play with the st_lambda, end_lambda parameters
iraf.emsao('@{}'.format(rvsao_bin_list), logfiles=rvsao_file, displot=interactive, report_mode=2,
contsub_plot="no", st_lambda=st_lambda, end_lambda=end_lambda, # vel_init="guess", czguess=czguess,
linesig=linesig, fixbad=fixbad, badlines=badlines)
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')
# Global python packages
import pyraf
from pyraf import iraf
import os, shutil, math, subprocess
import pyfits
# Local python packages
from iqutils import *
# IRAF modules
iraf.images()
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.specred()
iraf.rvsao()
# IRAF variables
yes=iraf.yes
no=iraf.no
INDEF=iraf.INDEF
hedit=iraf.hedit
imgets=iraf.imgets
imcombine=iraf.imcombine
# PyRAF setup
pyrafdir="python/pyraf/"
pyrafdir_key='PYRAFPARS'
if os.environ.has_key(pyrafdir_key):
pardir=os.environ[pyrafdir_key]
# Global python packages
import pyraf
from pyraf import iraf
import os, shutil, math, subprocess
import pyfits
# Local python packages
from iqutils import *
# IRAF modules
iraf.images()
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.specred()
iraf.rvsao()
# IRAF variables
yes = iraf.yes
no = iraf.no
INDEF = iraf.INDEF
hedit = iraf.hedit
imgets = iraf.imgets
imcombine = iraf.imcombine
# PyRAF setup
pyrafdir = "python/pyraf/"
pyrafdir_key = 'PYRAFPARS'
if os.environ.has_key(pyrafdir_key):
pardir = os.environ[pyrafdir_key]
