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 pipeline(productdir = "product", 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:
productdir -> String, containing the path to the 'product' directory. By
default, this is 'product'
mode -> Mode for velocity fitting. Currently the only option is H-Alpha
line fitting.
"""
#Print the welcome message
welcome_message()
#Set rest wave based on the mode called
if mode == "halpha": rest_wave = 6562.81
#Acquire the list of filenames from the product directory
fnlist = sorted(listdir(productdir))
#Convert the images to single-extension fits files
# This creates a new directory from scratch in order to preserve the
# original 'product' directory.
print "Converting images to single extension fits images..."
productlist = []
singextlist = []
for i in range(len(fnlist)):
if splitext(fnlist[i])[1] == ".fits":
productlist.append(join(productdir,fnlist[i]))
singextlist.append(join("singext","s"+fnlist[i]))
toggle = create_directory("singext")
if toggle:
make_single_extension(productlist,singextlist)
else: print "Skipping creation of single-extension fits images."
#Manual verification of fits images and headers
firstimage = openfits(singextlist[0])
verify = firstimage[0].header.get("fpverify")
firstimage.close()
if verify is None:
while True:
yn = raw_input("Manually verify image contents? (Recommended) (y/n) ")
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:
singextlist = verify_images(singextlist)
break
#Make separate lists of the different fits files
flatlist, list_of_objs, objlists, list_of_filts, filtlists = separate_lists(singextlist)
#Masking of pixels outside the aperture
firstimage = openfits(objlists[0][0])
axcen = firstimage[0].header.get("fpaxcen")
firstimage.close()
if axcen is None:
print "Masking pixels outside the RSS aperture..."
axcen, aycen, arad = get_aperture(objlists[0][0])
aperture_mask(singextlist,axcen,aycen,arad,maskvalue=0)
else: print "Images have already been aperture-masked."
#Cosmic ray removal
firstimage = openfits(objlists[0][0])
crtoggle = firstimage[0].header.get("fpcosmic")
firstimage.close()
if crtoggle is None:
print "Cosmic-ray correcting images..."
cosmic_ray_remove(singextlist)
else: print "Images have already been cosmic-ray corrected."
#Create uncertainty images for the object files
uncertlists = []
for i in range(len(objlists)):
uncertlists.append([])
for j in range(len(objlists[i])):
uncertlists[i].append(splitext(objlists[i][j])[0]+'_unc'+splitext(objlists[i][j])[1])
if not isfile(uncertlists[0][0]):
print "Generating uncertainty images..."
for i in range(len(objlists)):
for j in range(len(objlists[i])):
print "Writing uncertainty image "+uncertlists[i][j]
image = openfits(objlists[i][j])
image[0].data = np.sqrt(image[0].data)
image.writeto(uncertlists[i][j])
image.close()
else: print "Uncertainty images already exist."
#Image Flattening
firstimage = openfits(objlists[0][0])
flattog = firstimage[0].header.get("fpflat")
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 filtlist in filtlists: notflatlist+=filtlist
for uncertlist in uncertlists: notflatlist+=uncertlist
flatten(notflatlist,flatpath)
else: print "Skipping image flattening. (Not recommended!)"
else: print "Images have already been flattened."
#Measure stellar FWHMs
firstimage = openfits(objlists[0][0])
fwhm = firstimage[0].header.get("fpfwhm")
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 = openfits(objlists[i][0])
xcen = firstimage[0].header.get("fpxcen")
firstimage.close()
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
firstimage = openfits(objlists[0][0])
deghosted = firstimage[0].header.get("fpghost")
firstimage.close()
if deghosted is None:
print "Deghosting images..."
for i in range(len(objlists)):
for j in range(len(objlists[i])):
deghost(objlists[i][j],uncfn=uncertlists[i][j],g=0.04)
else: print "Images have already been deghosted."
#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]))
copyfile(uncertlists[i][j],join(list_of_objs[i].replace(" ", ""),split(uncertlists[i][j])[1]))
for j in range(len(objlists[i])):
objlists[i][j] = join(list_of_objs[i].replace(" ", ""),split(objlists[i][j])[1])
uncertlists[i][j] = join(list_of_objs[i].replace(" ", ""),split(uncertlists[i][j])[1])
#Update the filter lists (THIS IS TERRIBLE AND I SHOULD HAVE USED POINTERS AND I'M SO SORRY)
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 = openfits(objlists[i][0])
aligned = firstimage[0].header.get("fpphot")
firstimage.close()
if aligned is None:
print "Aligning and normalizing images for object "+list_of_objs[i]+"..."
align_norm(objlists[i],uncertlist=uncertlists[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
for i in range(len(list_of_filts)):
#Do a separate calibration for each filter
firstimage = openfits(filtlists[i][0])
calf = firstimage[0].header.get("fpcalf")
firstimage.close()
if not(calf is None):
while True:
yn = raw_input("Wavelength solution already found for filter "+list_of_filts[i]+". Redo it? (y/n) ")
if "n" in yn or "N" in yn:
break
elif "y" in yn or "Y" in yn:
fit_wave_soln(filtlists[i])
break
else: fit_wave_soln(filtlists[i])
#Sky ring removal
for i in range(len(objlists)):
#Check to see if sky rings have already been removed
firstimage = openfits(objlists[i][0])
deringed = firstimage[0].header.get("fpdering")
firstimage.close()
if deringed is None:
print "Subtracting sky rings for object "+list_of_objs[i]+"..."
sub_sky_rings(objlists[i],[join(list_of_objs[i].replace(" ", ""),"median.fits")]*len(objlists[i]))
else: print "Sky ring subtraction already done for object "+list_of_objs[i]
#Creation of data cube and convolution to uniform PSF
# Data cube is created in a separate directory, which is created if it
# does not already exist, or overwritten if the user so chooses
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:
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 = openfits(objlists[i][j])
fwhm = image[0].header["fpfwhm"]
if j==0: largestfwhm = fwhm
if fwhm>largestfwhm: largestfwhm=fwhm
image.close()
while True:
user_fwhm = raw_input("Enter desired final fwhm or leave blank to use default ("+str(largestfwhm)+" pix) ")
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
for j in range(len(objlists[i])):
make_final_image(objlists[i][j], #Input image
join(list_of_objs[i].replace(" ", "")+"_cube",splitext(split(objlists[i][j])[1])[0]+"_inty"+splitext(split(objlists[i][j])[1])[1]), #Output image
join(list_of_objs[i].replace(" ", "")+"_cube",splitext(split(objlists[i][j])[1])[0]+"_wave"+splitext(split(objlists[i][j])[1])[1]), #Output wave image
desired_fwhm, #Desired final FWHM
input_uncert_image = uncertlists[i][j], #Input uncertainty image
output_uncert_image = join(list_of_objs[i].replace(" ", "")+"_cube",split(uncertlists[i][j])[1]), #Output uncertainty image
clobber=True)
