def pysalt(fs=None):
# Run the pysalt pipeline on the raw data.
if fs is None:
fs = glob('P*.fits')
if len(fs) == 0:
print "WARNING: No raw files to run PySALT pre-processing."
return
# Copy the raw files into a raw directory
if not os.path.exists('raw'):
os.mkdir('raw')
if not os.path.exists('work'):
os.mkdir('work')
for f in fs:
shutil.copy2(f, 'raw/')
shutil.move(f, 'work/')
iraf.cd('work')
# Run each of the pysalt pipeline steps deleting temporary files as we go
# saltprepare
iraf.unlearn(iraf.saltprepare)
# Currently, there is not a bad pixel mask provided by SALT
# so we don't create one here.
iraf.saltprepare(images='P*.fits', clobber=True, mode='h')
for f in glob('P*.fits'):
os.remove(f)
# saltgain
iraf.unlearn(iraf.saltgain)
# Multiply by the gain so that everything is in electrons.
iraf.saltgain(images='pP*.fits',
gaindb=pysaltpath + '/data/rss/RSSamps.dat',
mult=True, usedb=True, mode='h')
for f in glob('pP*.fits'):
os.remove(f)
# write a keyword in the header keyword gain = 1 in each amplifier
fs = glob('gpP*.fits')
for f in fs:
for i in range(1, 7):
pyfits.setval(f, 'GAIN', ext=i, value=1.0)
# saltxtalk
iraf.unlearn(iraf.saltxtalk)
iraf.saltxtalk(images='gpP*.fits', clobber=True, usedb=True,
xtalkfile=pysaltpath + '/data/rss/RSSxtalk.dat', mode='h')
for f in glob('gpP*.fits'):
os.remove(f)
# saltbias
iraf.unlearn(iraf.saltbias)
iraf.saltbias(images='xgpP*.fits', clobber=True, mode='h')
for f in glob('xgpP*.fits'):
os.remove(f)
# Put all of the newly created files into the pysalt directory
if not os.path.exists('pysalt'):
os.mkdir('pysalt')
for f in glob('bxgpP*.fits'):
shutil.move(f, 'pysalt')
iraf.cd('..')
def pipeline(rawdir="raw", mode="halpha"):
"""Runs successive steps of the saltfp data reduction, checking along the
way to see if each step was successful. This is the main driver program of
the SALT Fabry-Perot pipeline.
Inputs:
rawdir -> String, containing the path to the 'raw' directory. By
default, this is 'raw'
mode -> Mode for velocity fitting. Currently the only option is H-Alpha
line fitting.
"""
# Set rest wave based on the mode called
if mode == "halpha":
rest_wave = 6562.81
# Create product directory
if isdir("product"):
while True:
yn = raw_input("Product directory already exists. " + "Recreate it? (y/n) ")
if "n" in yn or "N" in yn:
break
elif "y" in yn or "Y" in yn:
# Confirmation
yn = raw_input("Are you sure? This takes a while. (y/n) ")
if ("y" in yn or "Y" in yn) and not ("n" in yn or "N" in yn):
rmtree("product")
break
if not isdir("product"):
# Acquire the list of filenames from the raw directory
fnlist = sorted(listdir(rawdir))
for i in range(len(fnlist)):
fnlist[i] = join(rawdir, fnlist[i])
# Run the first two steps of imred on the first image
iraf.pysalt(_doprint=0)
iraf.saltred(_doprint=0)
iraf.saltprepare(
fnlist[0],
"temp.fits",
"",
createvar=False,
badpixelimage="",
clobber=True,
logfile="temp.log",
verbose=True,
)
iraf.saltbias(
"temp.fits",
"temp.fits",
"",
subover=True,
trim=True,
subbias=False,
masterbias="",
median=False,
function="polynomial",
order=5,
rej_lo=3.0,
rej_hi=5.0,
niter=10,
plotover=False,
turbo=False,
clobber=True,
logfile="temp.log",
verbose=True,
)
# Create the bad pixel mask
image = fits.open("temp.fits")
for i in range(1, len(image)):
mask = image[i].data != image[i].data
image[i].data = 1 * mask
image.writeto("badpixmask.fits", clobber="True")
image.close()
# Remove temporary files
remove("temp.fits")
remove("temp.log")
# Run the raw images through the first few data reduction pipeline
# steps
imred(fnlist, "product", bpmfile="badpixmask.fits")
# Delete the temporary bad pixel mask
remove("badpixmask.fits")
# Move these raw images into the product directory
mkdir("product")
fnlist = sorted(listdir("."))
for i in range(len(fnlist)):
if "mfxgbpP" in fnlist[i] and ".fits" in fnlist[i]:
move(fnlist[i], join("product", fnlist[i]))
# List of files in the product directory
fnlist = sorted(listdir("product"))
for i in range(len(fnlist)):
fnlist[i] = join("product", fnlist[i])
# Manual verification of fits images and headers
firstimage = FPImage(fnlist[0])
verify = firstimage.verifytog
firstimage.close()
if verify is None:
while True:
prompt = "Manually verify image contents? (Recommended) (y/n) "
yn = raw_input(prompt)
if "n" in yn or "N" in yn:
print ("Skipping manual verification of image contents " + "(Not recommended)")
break
if "y" in yn or "Y" in yn:
fnlist = verify_images(fnlist)
break
# Make separate lists of the different fits files
(flatlist, list_of_objs, objlists, list_of_filts, filtlists) = separate_lists(fnlist)
# Masking of pixels outside the aperture
firstimage = FPImage(objlists[0][0])
axcen = firstimage.axcen
firstimage.close()
if axcen is None:
print "Masking pixels outside the RSS aperture..."
axcen, aycen, arad = get_aperture(objlists[0][0])
aperture_mask(fnlist, axcen, aycen, arad)
else:
print "Images have already been aperture-masked."
# Masking bad pixels from external region file
for objlist in objlists:
for i in range(len(objlist)):
if isfile(splitext(split(objlist[i])[1])[0] + ".reg"):
print ("Adding regions from file " + splitext(split(objlist[i])[1])[0] + ".reg to the bad pixel mask.")
mask_regions(objlist[i], splitext(split(objlist[i])[1])[0] + ".reg")
# Measure stellar FWHMs
firstimage = FPImage(objlists[0][0])
fwhm = firstimage.fwhm
firstimage.close()
if fwhm is None:
dofwhm = True
else:
while True:
yn = raw_input("Seeing FWHM has already been measured. " + "Redo this? (y/n) ")
if "n" in yn or "N" in yn:
dofwhm = False
break
elif "y" in yn or "Y" in yn:
dofwhm = True
break
if dofwhm:
print "Measuring seeing FWHMs..."
for objlist in objlists:
measure_fwhm(objlist)
# Find image centers using ghost pairs
for i in range(len(objlists)):
firstimage = FPImage(objlists[i][0])
xcen = firstimage.xcen
deghosted = firstimage.ghosttog
firstimage.close()
if deghosted is None:
if xcen is None:
ghosttog = True
else:
while True:
yn = raw_input(
"Optical centers already measured for " + "object " + list_of_objs[i] + ". Redo this? (y/n) "
)
if "n" in yn or "N" in yn:
ghosttog = False
break
elif "y" in yn or "Y" in yn:
ghosttog = True
break
if ghosttog:
print (
"Identifying optical centers for object "
+ list_of_objs[i]
+ ". This may take a while for crowded fields..."
)
find_ghost_centers(objlists[i])
# Deghost images
for i in range(len(objlists)):
firstimage = FPImage(objlists[i][0])
deghosted = firstimage.ghosttog
firstimage.close()
if deghosted is None:
print "Deghosting images for object " + list_of_objs[i] + "..."
for j in range(len(objlists[i])):
deghost(objlists[i][j])
else:
print ("Images for object " + list_of_objs[i] + " have already been deghosted.")
# Image Flattening
firstimage = FPImage(objlists[0][0])
flattog = firstimage.flattog
firstimage.close()
if flattog is None:
print "Flattening images..."
if len(flatlist) == 0:
while True:
print "Uh oh! No flatfield exposure found!"
flatpath = raw_input("Enter path to external flat image: " + "(leave blank to skip flattening) ")
if flatpath == "" or isfile(flatpath):
break
else:
combine_flat(flatlist, "flat.fits")
flatpath = "flat.fits"
if flatpath != "":
notflatlist = []
for objlist in objlists:
notflatlist += objlist
flatten(notflatlist, flatpath)
else:
print "Skipping image flattening. (Not recommended!)"
else:
print "Images have already been flattened."
# Make separate directories for each object.
# This is the first bit since 'singext' to create a new directory, because
# this is the first point where it's really necessary to start treating the
# images from different tracks very differently.
for i in range(len(objlists)):
if isdir(list_of_objs[i].replace(" ", "")):
while True:
yn = raw_input("A directory for object " + list_of_objs[i] + " already exists. Recreate? (y/n) ")
if "n" in yn or "N" in yn:
do_copy = False
break
elif "y" in yn or "Y" in yn:
do_copy = True
rmtree(list_of_objs[i].replace(" ", ""))
break
else:
do_copy = True
if do_copy:
mkdir(list_of_objs[i].replace(" ", ""))
for j in range(len(objlists[i])):
copyfile(objlists[i][j], join(list_of_objs[i].replace(" ", ""), split(objlists[i][j])[1]))
for j in range(len(objlists[i])):
objlists[i][j] = join(list_of_objs[i].replace(" ", ""), split(objlists[i][j])[1])
# Update the filter lists
for i in range(len(filtlists)):
for j in range(len(filtlists[i])):
for k in range(len(objlists)):
for l in range(len(objlists[k])):
if split(filtlists[i][j])[1] == split(objlists[k][l])[1]:
filtlists[i][j] = objlists[k][l]
# Image alignment and normalization
for i in range(len(objlists)):
firstimage = FPImage(objlists[i][0])
aligned = firstimage.phottog
firstimage.close()
if aligned is None:
print ("Aligning and normalizing images for object " + list_of_objs[i] + "...")
align_norm(objlists[i])
else:
print ("Images for object " + list_of_objs[i] + " have already been aligned and normalized.")
# Make a median image for each object
for i in range(len(objlists)):
if isfile(join(list_of_objs[i].replace(" ", ""), "median.fits")):
while True:
yn = raw_input("Median image for object " + list_of_objs[i] + " already exists. Replace it? (y/n) ")
if "n" in yn or "N" in yn:
break
elif "y" in yn or "Y" in yn:
make_median(objlists[i], join(list_of_objs[i].replace(" ", ""), "median.fits"))
break
else:
make_median(objlists[i], join(list_of_objs[i].replace(" ", ""), "median.fits"))
# Wavelength calibrations
all_rings_list = []
for i in range(len(list_of_filts)):
all_rings_list = all_rings_list + filtlists[i]
firstimage = FPImage(all_rings_list[0])
calf = firstimage.calf
firstimage.close()
if not (calf is None):
while True:
yn = raw_input("Wavelength solution already found. " + "Redo it? (y/n) ")
if "n" in yn or "N" in yn:
break
elif "y" in yn or "Y" in yn:
fit_wave_soln(all_rings_list)
break
else:
fit_wave_soln(all_rings_list)
# Sky ring removal
for i in range(len(objlists)):
for j in range(len(objlists[i])):
# Check to see if sky rings have already been removed
image = FPImage(objlists[i][j])
deringed = image.ringtog
image.close()
if deringed is None:
print "Subtracting sky rings for image " + objlists[i][j]
sub_sky_rings([objlists[i][j]], [join(list_of_objs[i].replace(" ", ""), "median.fits")])
else:
print ("Sky ring subtraction already done for image " + objlists[i][j])
# Creation of data cube and convolution to uniform PSF
for i in range(len(objlists)):
if isdir(list_of_objs[i].replace(" ", "") + "_cube"):
while True:
yn = raw_input("A data cube for object " + list_of_objs[i] + " already exists. Recreate? (y/n) ")
if "n" in yn or "N" in yn:
do_create = False
break
elif "y" in yn or "Y" in yn:
# Confirmation
yn = raw_input("Are you sure? This takes a while. (y/n) ")
if ("y" in yn or "Y" in yn) and not ("n" in yn or "N" in yn):
do_create = True
rmtree(list_of_objs[i].replace(" ", "") + "_cube")
break
else:
do_create = True
if do_create:
mkdir(list_of_objs[i].replace(" ", "") + "_cube")
for j in range(len(objlists[i])):
image = FPImage(objlists[i][j])
fwhm = image.fwhm
if j == 0:
largestfwhm = fwhm
if fwhm > largestfwhm:
largestfwhm = fwhm
image.close()
while True:
prompt = "Enter desired final fwhm or leave blank to use" + " default (" + str(largestfwhm) + " pix) "
user_fwhm = raw_input(prompt)
if user_fwhm == "":
user_fwhm = largestfwhm
break
else:
try:
user_fwhm = float(user_fwhm)
except ValueError:
print "That wasn't a valid number..."
else:
if user_fwhm < largestfwhm:
print ("Final fwhm must exceed " + str(largestfwhm) + " pixels.")
else:
break
desired_fwhm = user_fwhm * 1.01
for j in range(len(objlists[i])):
make_final_image(
objlists[i][j],
join(list_of_objs[i].replace(" ", "") + "_cube", split(objlists[i][j])[1]),
desired_fwhm,
clobber=True,
)
# Get final lists for the velocity map fitting for each object
final_lists = []
for i in range(len(list_of_objs)):
final_lists.append([])
for j in range(len(objlists[i])):
final_lists[i].append(join(list_of_objs[i].replace(" ", "") + "_cube", split(objlists[i][j])[1]))
# Shift to solar velocity frame
for i in range(len(list_of_objs)):
firstimage = FPImage(final_lists[i][0])
velshift = firstimage.solarvel
firstimage.close()
if velshift is None:
print ("Performing solar velocity shift for object " + list_of_objs[i] + "...")
solar_velocity_shift(final_lists[i], rest_wave)
else:
print ("Solar velocity shift for object " + list_of_objs[i] + " already done.")
if not do_velmap:
sys.exit("Velocity map not made - Voigt-fitting software not found.")
# Velocity map fitting
for i in range(len(list_of_objs)):
if isfile(join(list_of_objs[i].replace(" ", "") + "_cube", "velocity.fits")):
while True:
yn = raw_input("Velocity map already fitted for object " + list_of_objs[i] + ". Redo this? (y/n) ")
if "n" in yn or "N" in yn:
domap = False
break
elif "y" in yn or "Y" in yn:
# Confirmation
yn = raw_input("Are you sure? This takes a while. (y/n) ")
if ("y" in yn or "Y" in yn) and not ("n" in yn or "N" in yn):
domap = True
break
else:
domap = True
if domap:
print "Fitting velocity map for object " + list_of_objs[i] + "..."
if mode == "halpha":
fit_velmap_ha_n2_mode(final_lists[i], list_of_objs[i].replace(" ", "") + "_cube", clobber=True)
# Clean velocity map
for i in range(len(list_of_objs)):
make_clean_map(list_of_objs[i].replace(" ", "") + "_cube", clobber=True)
def pysalt(fs=None):
# Run the pysalt pipeline on the raw data.
if fs is None:
fs = glob('P*.fits')
if len(fs) == 0:
print "WARNING: No raw files to run PySALT pre-processing."
return
# Copy the raw files into a raw directory
if not os.path.exists('raw'):
os.mkdir('raw')
if not os.path.exists('work'):
os.mkdir('work')
for f in fs:
shutil.copy2(f, 'raw/')
shutil.move(f, 'work/')
iraf.cd('work')
# Run each of the pysalt pipeline steps deleting temporary files as we go
# saltprepare
iraf.unlearn(iraf.saltprepare)
# Currently, there is not a bad pixel mask provided by SALT
# so we don't create one here.
iraf.saltprepare(images='P*.fits', clobber=True, mode='h')
for f in glob('P*.fits'):
os.remove(f)
# saltgain
iraf.unlearn(iraf.saltgain)
# Multiply by the gain so that everything is in electrons.
iraf.saltgain(images='pP*.fits',
gaindb=pysaltpath + '/data/rss/RSSamps.dat',
mult=True, usedb=True, mode='h')
for f in glob('pP*.fits'):
os.remove(f)
# write a keyword in the header keyword gain = 1 in each amplifier
fs = glob('gpP*.fits')
for f in fs:
for i in range(1, 7):
pyfits.setval(f, 'GAIN', ext=i, value=1.0)
# saltxtalk
iraf.unlearn(iraf.saltxtalk)
iraf.saltxtalk(images='gpP*.fits', clobber=True, usedb=True,
xtalkfile=pysaltpath + '/data/rss/RSSxtalk.dat', mode='h')
for f in glob('gpP*.fits'):
os.remove(f)
# saltbias
iraf.unlearn(iraf.saltbias)
iraf.saltbias(images='xgpP*.fits', clobber=True, mode='h')
for f in glob('xgpP*.fits'):
os.remove(f)
# Put all of the newly created files into the pysalt directory
if not os.path.exists('pysalt'):
os.mkdir('pysalt')
for f in glob('bxgpP*.fits'):
shutil.move(f, 'pysalt')
iraf.cd('..')