def trim(args):
# Define region to be trimmed
y0, y1, x0, x1 = args.region
# args.output should be a list of output names. If they do not exist, the outputs should be the same as the
# inputs with whatever suffix, if any, the user gave
if not args.output:
args.output = [utilities.add_suffix_prefix(im_name, suffix=args.suffix) for im_name in args.input]
# Do the actual trimming. We will do it first into a temporary file, then copy it into args.output. This is just
# in case the output and input filenames are the same, or if the output exists. IRAF will not overwrite!
for im_name, new_name in zip(args.input, args.output):
basename = os.path.splitext(os.path.basename(im_name))[0]
_, temp_output = tempfile.mkstemp(prefix=basename, suffix=".fits")
os.unlink(temp_output )
with utilities.tmp_mute():
imcopy(im_name + "[{0}:{1}, {2}:{3}]".format(x0, x1, y0, y1), temp_output)
shutil.move(temp_output, new_name)
# If a mask exists, trim exactly equally
if args.mask_key:
maskname = fits.getheader(im_name)[args.mask_key]
with fits.open(maskname, 'readonly') as mask_im:
mask_im[0].data = mask_im[0].data[y0:y1+1, x0:x1+1]
mask_output = utilities.replace_extension(new_name, ".fits.msk")
fits.writeto(mask_output, mask_im[0].data, mask_im[0].header, clobber=True)
utilities.header_update_keyword(new_name, "MASK", os.path.abspath(mask_output),
comment="Name of mask image. ")
return args.output
def mask(args):
for image, output in zip(args.image, args.output):
im = astroim.Astroim(image)
# If mask does not exist
if not im.mask_name:
im.HDUList_mask = build_mask(im)
im.mask_name = utils.replace_extension(im.im_name, ".fits.msk")
im.chips = im._get_chips()
im._copy_wcs_to_mask()
im = minmax_mask(im, min_val=args.minval, max_val=args.maxval, mask_value=args.false_val)
# If circular field of view within rectangular image:
if args.circular:
im = mask_circular(im, mask_value=args.outside_val, margin=args.margin)
# Star masking fitting sky
if args.stars: # if stars in the image
im = max_sigma_clip(im, n_sigma=3, mask_value=args.false_val)
# Save mask image
im.HDUList_mask[0].header.add_comment("Mask for image {0}".format(image))
im.HDUList_mask.writeto(output, clobber=True)
# Include comment in the header
im.primary_header.hdr["MASK"] = output
im.write()
return None
def main(im_name, ra=None, dec=None, FoV=None):
if ra == None or dec == None:
if FoV == None:
FoV = 20 # Largest image I've seen, 20 degrees, just in case
# User passed the keywords?
try:
ra, dec = utilities.get_from_header(im_name, ra, dec)
argunents = ["--ra", ra, "--dec", dec, "--radius", float(FoV)/2.]
except KeyError:
arguments = []
# User passed the numbers
try:
ra, dec = float(ra), float(dec)
argunents = ["--ra", ra, "--dec", dec, "--radius", float(FoV)/2.]
except ValueError:
arguments = []
arguments = arguments + ["solve-field", "--no-plots", "--no-fits2fits",
"--use-sextractor", "--code-tolerance", "0.01",
"--overwrite", im_name]
subprocess.call(arguments)
# Sort things out, clean up...
solved = utilities.replace_extension(im_name, "solved")
output_name = utilities.replace_extension(im_name, "new")
assert os.path.exists(output_name)
shutil.move(output_name, im_name)
# Update old WCS system PC matrices and so on to avoid confusion
utilities.update_WCS(im_name, im_name)
# Build a catalogue with the coordinates of the stars found in
# X and Y, RA and DEC and the flux of the stars
corr_file = utilities.replace_extension(im_name, "corr")
table = fits.open(corr_file)[1]
cat_radec = utilities.replace_extension(im_name, "radec")
f = open(cat_radec, "w")
for line in table.data:
f.write(str(line[2]) + " " + str(line[3]) + "\n")
f.close()
def main(arguments = None):
# Pass arguments to variable args
if arguments == None:
arguments = sys.argv[1:]
args = parser.parse_args(arguments)
if not args.output:
args.output = [utils.replace_extension(im, ".fits.msk") for im in args.image]
# True_val and false_val can not be the same
if args.true_val == args.false_val:
sys.exit("\n\n ERROR: true_val and false_val are the same value: " + \
str(args.false_val) + " Use --true_val and --false_val \n\n")
# Call combine, keep name of the file created
masknames = mask(args)
return masknames
def main(arguments = None):
# Pass arguments to variable args
if arguments == None:
arguments = sys.argv[1:]
args = parser.parse_args(arguments)
# Option 1: the user did not provide any catalogues, because they are named like the images, but with '.radec' extension
# Option 2: the user provided a single catalogue, because all the images contain the same object
# Option 3: the user provided an invalid number of catalogues for the number of images.
if args.cat is None:
args.cat = [utilities.replace_extension(im_name, ".radec") for im_name in args.input]
if not all([os.path.exists(cat_name) for cat_name in args.cat]):
sys.exit("Catalogues not found! Check the --cat option in the description: type estimate_seeing.py -h")
elif len(args.cat) == 1:
args.cat *= len(args.input)
elif len(args.input) != len(args.cat):
sys.exit("\n\n number of star catalogues and input images do not coincide \n ")
calculate_seeing(args)
return None
def main(Ha_name, rgunn_name, scaling_factor, zp_Ha, T_Ha, T_rGunn):
# Tell the user to load regions for the galaxy
galaxy_messg = "\n Create/load region(s) in ds9 to enclose the galaxy. "+\
" Save it for latter use. Then hit the 'OK' button!"
galaxy_region = mask_from_ds9(rgunn_name, galaxy_messg)
# Sky regions
sky_messg = "\n Now do the same for sky region (or regions!) "
sky_region = mask_from_ds9(rgunn_name, sky_messg)
# Stars to model PSF
mod_stars_catalogue = utils.replace_extension(rgunn_name, ".model_stars")
mod_stars_messg = "\n Select nice isolated non-saturated stars to model the PSF. "
mod_stars_region = positions_of_stars_from_ds9(rgunn_name, mod_stars_messg, mod_stars_catalogue)
# Stars to be subtracted
subt_stars_catalogue = utils.replace_extension(rgunn_name, ".subt_stars")
subt_stars_messg = "\n Finally, select the stars to be subtracted from the images"
subt_stars_region = positions_of_stars_from_ds9(rgunn_name, subt_stars_messg, subt_stars_catalogue)
# Now remove the stars from the images
output_Ha = utils.add_suffix_prefix(Ha_name, suffix='-s')
extract_stars.main(arguments=['--model_stars', mod_stars_catalogue,
"--subt_stars", subt_stars_catalogue,
'--coords', 'world',
'--output', output_Ha,
Ha_name])
output_rgunn = utils.add_suffix_prefix(rgunn_name, suffix='-s')
extract_stars.main(arguments=['--model_stars', mod_stars_catalogue,
"--subt_stars", subt_stars_catalogue,
'--coords', 'world',
'--output', output_rgunn,
rgunn_name])
# Read images
image_Ha = fits.open(output_Ha)
Ha_data = np.array(image_Ha[0].data, dtype=np.float64)
Ha_header = image_Ha[0].header
Ha_shape = Ha_data.shape
image_R = fits.open(output_rgunn)
R_data = np.array(image_R[0].data, dtype=np.float64)
R_header = image_R[0].header
R_shape = R_data.shape
# Halpha image: get galaxy mask, sky mask, subtract sky from galaxy flux
Ha_gal_mask = galaxy_region.as_imagecoord(Ha_header).get_mask(shape=Ha_shape)
Ha_sky_mask = sky_region.as_imagecoord(Ha_header).get_mask(shape=Ha_shape)
Ha_sky_flux = np.median(Ha_data[Ha_sky_mask == 1])
Ha_data_nosky = Ha_data - Ha_sky_flux
Ha_and_R_counts = np.sum( Ha_data_nosky[Ha_gal_mask == 1])
print "Narrow Ha filter counts: ", Ha_and_R_counts
# Same for R image: get galaxy mask, sky mask, subtract sky from galaxy flux
R_gal_mask = galaxy_region.as_imagecoord(R_header).get_mask(shape=R_shape)
R_sky_mask = sky_region.as_imagecoord(R_header).get_mask(shape=R_shape)
R_sky_flux = np.median(R_data[R_sky_mask == 1])
R_data_nosky = R_data - R_sky_flux
R_counts = np.sum( R_data_nosky[R_gal_mask == 1])
print "R filter counts: ", R_counts
# Halpha is basically the subtractiong of the counts in Halpha filter minus the scaled R counts
# but more precisely, the contrinbution of Halpha line to the filter rGunn should be corrected.
# For:
# - a transmittance T(Gunn) of redshifted Halpha in the rGunn filter
# - T(Ha_filter) the transmittance of redshifted Halpha in the Halpha narrow filter
# - scaling_factor the factor of scale between counts in filter rGunn and in the Ha filter (typically from stars)
# - rgunn_scaled the number of counts in the Gunn filter, already scaled to match Halpha
# - Ha_filter is the counts in the narrow Ha filter, which obviously contain a contribution from R.
# - Halpha the number of counts of the Halpha line in the Halpha filter (the contribution of Halpha alone)
#
# we would need to subtract the Halpha contribution to rGunn before we scale it, but since we are starting
# already from a scaled version of rGunn, we need to divide the contribution from Halpha by the scaling factor.
# The system of equations would be:
#
# R_counts = rgunn_scaled - Halpha * T(Gunn)/T(Ha_filter) / scaling_factor
# Halpha = Ha_filter - R_counts
#
# with solution:
# Halpha = (Ha_filter - rgunn_scaled) * scaling_factor/(scaling_factor - T(Gunn)/T(Halpha)
#
# which gives, for a typical scaling factor of ~11 and similar transmittances in both filters a correction
# of the order of ~10%.
Halpha = (Ha_and_R_counts - R_counts * scaling_factor) / (1 - scaling_factor * T_rGunn / T_Ha)
print "Halpha counts: ", Halpha
print Halpha / T_Ha * 10**(-zp_Ha/2.5)
mask = np.logical_or(Ha_gal_mask, Ha_sky_mask)
d = ds9.ds9()
d.set_np2arr(Ha_data * mask, dtype=np.float64)
for index, image in enumerate(list_images["filename"]):
if list_images["type"][index] in ("cig", "standards", "clusters"):
# Victor Terron has promissed changing dirs will soon be unnecessary
curdir = os.path.abspath(os.curdir)
os.chdir(lemon_dir)
import lemon.seeing as seeing
seeing.main(arguments=["--margin", "0", "--filename", '', "--suffix",
"-s", image, os.path.split(image)[0] ])
newname = utilities.add_suffix_prefix(image, suffix = "-s")
os.chdir(curdir)
list_images["filename"][index] = newname
# While running lemon.seeing a sextractor catalogue is produced.
catalog = fits.getheader(newname)["SEX CATALOG"]
catalog_newname = utilities.replace_extension(newname, ".cat")
shutil.copy(catalog, catalog_newname)
# Damn FITS format and its constraints!
short_name = os.path.split(catalog_newname)[1]
utilities.header_update_keyword(newname, "SEX CATALOG", short_name)
print "Estimate sky for images of CIG(s), standard(s) and cluster(s) "
for index, image in enumerate(list_images["filename"]):
if list_images["type"][index] in ["cig","standards","clusters"]:
find_sky.main(arguments=[list_images["filename"][index]])
print "Detecting objects for images of CIG(s), standard(s) and cluster(s)"
# This part follows the example in the webpage:
#http://www.lancesimms.com/programs/Python/pyraf/daofind.py
for index, image in enumerate(list_images["filename"]):
if list_images["type"][index] in ["cig","standards","clusters"]:
def include_wcs(args):
# Copy input names into output names
output_names = args.input[:]
for ii, im_name in enumerate(args.input):
# If it is a domeflat, skyflat or bias image, calculating the astrometry makes little sense
im = astroim.Astroim(im_name)
if im.target.objtype in ["bias", "domeflat", "skyflat", "flat"]:
continue
# Prepare my output file for the resulting image
if args.suffix:
new_file = utilities.add_suffix_prefix(im_name, suffix=args.suffix)
elif args.overwrite:
new_file = im_name
# Copy input image into a temporary file, so that we can modify it freely, for example, remove cosmics
# or filter it (to be done).
basename = get_basename(im_name)
_, input_image = tempfile.mkstemp(prefix=basename, suffix=".fits")
shutil.copy2(im_name, input_image)
# Remove cosmics
if args.cosmics:
perform_cosmic_removal(input_image)
# The output of the WCS process of astrometry.net will go to a .new file, the coordinates to a .coor
output_wcs = utilities.replace_extension(input_image, ".new")
solved_file = utilities.replace_extension(input_image, ".solved")
corrfile = utilities.replace_extension(input_image, ".cor")
# Try first with the defaults of astrometry
arguments_def = ["solve-field", "--no-plots", "--no-fits2fits", "--dir", "/tmp", "--overwrite",
"--new-fits", output_wcs, "--corr", corrfile, "--cpulimit", "1", input_image]
try: # Try to add the RA, DEC, Radius options to constrain the search
ra, dec = im.primary_header.get(im.primary_header.RAk, im.primary_header.DECk)
ra, dec = utilities.sex2deg(ra, dec)
arguments = arguments_def + ["--ra", str(ra), "--dec", str(dec), "--radius", str(args.radius),
"--cpulimit", "20"]
except:
arguments = arguments_def
print "Trying to find WCS with astrometry standard keywords. "
with utilities.tmp_mute():
subprocess.call(arguments)
# Now we will try using sextractor
build_default_sex(args)
# To avoid having too much residual crap in the folder, the output of astrometry will go to tmp (--dir /tmp).
arguments0 = ["solve-field", "--no-plots", "--no-fits2fits", "--use-sextractor", "--dir", "/tmp",
"--x-column", "X_IMAGE", "--y-column", "Y_IMAGE", "--sort-column", "MAG_AUTO",
"--sort-ascending", "--sextractor-config", os.path.join(repipy_path, "default.sex"),
"--overwrite", "--new-fits", output_wcs, "--corr", corrfile, input_image]
arguments0 += args.extras
try: # Try to add the RA, DEC, Radius options to constrain the search
ra, dec = im.primary_header.get(im.primary_header.RAk, im.primary_header.DECk)
ra, dec = utilities.sex2deg(ra, dec)
arguments = arguments0 + ["--ra", str(ra), "--dec", str(dec), "--radius", str(args.radius),
"--cpulimit", "20"]
except:
arguments = arguments0
# Run astrometry, in case of not solving it on the first attempt, try fitting freely (no RA, DEC used)
if not os.path.exists(solved_file):
subprocess.call(arguments)
if not os.path.exists(solved_file):
subprocess.call(arguments0)
# Only if we have a solution
if os.path.exists(solved_file):
# copy the input file into the new file if they are not the same
if os.path.abspath(im_name) != os.path.abspath(new_file):
shutil.copy2(im_name, new_file)
# get WCS from the resulting file, only the part added by astrometry.net
with fits.open(output_wcs) as file_wcs:
hdr_wcs = file_wcs[0].header
ind = hdr_wcs.values().index('Original key: "END"') # new part added by astrometry
hdr_wcs = hdr_wcs[ind:]
# Add that header to the original one of the image
with fits.open(new_file, 'update') as new_im:
new_im[0].header += hdr_wcs
new_im.flush()
# Build a catalogue of RA, DEC for the stars found
with fits.open(corrfile) as table:
cat_radec = utilities.replace_extension(new_file, "radec")
with open(cat_radec, "w") as f:
for ra, dec in zip(table[1].data["field_ra"], table[1].data["field_dec"]):
f.write(str(ra) + " " + str(dec) + "\n")
output_names[ii] = new_file
# If --remove_original is True
if args.remove_orig and not args.overwrite:
os.unlink(im_name)
return output_names
if list_images["type"][index] in ["cig", "standards", "clusters"]:
# Get date, RA, DEC from images.
time, RA_current, DEC_current = utilities.get_from_header(im, datek,
rak,deck)
RA, DEC = utilities.precess_to_2000(RA_current, DEC_current, time)
RA, DEC, radius = str(RA), str(DEC), str(FoV/2.5)
# Calculate WCS using sextractor
subprocess.call(["solve-field", "--no-plots", "--no-fits2fits",
"--ra", RA, "--dec", DEC, "--radius", radius,
"--depth", "1-30", "--depth", "1-50", "--depth",
"1-100", "--depth", "10,20,30,40,50,60,70,80,90,100",
"--use-sextractor", "--code-tolerance", "0.01",
"--overwrite", im])
solved = utilities.replace_extension(im, "solved")
# Case sextractor couldn't do it, try with astrometry.net's own routine
if not os.path.exists(solved):
subprocess.call(["solve-field", "--no-plots", "--no-fits2fits",
"--ra", RA, "--dec", DEC, "--radius", radius,
"--depth", "1-30", "--depth", "1-50", "--depth",
"1-100", "--depth", "10,20,30,40,50,60,70,80,90,100",
"--code-tolerance", "0.002", "--overwrite",
im])
output_name = utilities.replace_extension(im, "new")
shutil.move(output_name, im)
# Update old WCS system PC matrices and so on to avoid confusion
utilities.update_WCS(im, im)
in_pattern["blanks"] = "^(?P<name>blank)_(?P<date>\d{8})_(?P<filt>.*)_" +\
"(?P<exp_num>\d{3})(?P<rest>.*-c\.fits)$"
list_images = utilities.locate_images2(dir, in_pattern)
return list_images
list_images = search_images(directory)
print "For each object and filter, do photometry on all images"
AllObj_set = set(x for x,t in zip(list_images["objname"], list_images["type"]) if t in ["standards", "cig", "clusters"])
for obj in AllObj_set: # for each of the standards
obj_images = list(list_images["filename"][np.where( (list_images["objname"] == obj))])
output_db = os.path.join(directory, obj+".db")
utilities.if_exists_remove(output_db)
coord_file = utilities.replace_extension(obj_images[0], "radec")
photometry.main(arguments=["--maximum", str(max_counts), "--uik", "", "--margin", "20", "--gaink", gaink,
"--aperture", "4.", "--annulus", "6", "--dannulus", "2", "--individual-fwhm", "--objectk", objectk,
"--filterk", filterk, "--datek", datek, "--expk", exptimek, "--fwhmk", seeingk, "--airmk", airmassk,
"--coordinates", coord_file, obj_images[0]] + obj_images + [output_db])
print "Find extinction coefficient from photometry of standards"
standards_set = set(x for x,t in zip(list_images["objname"], list_images["type"]) if t == "standards")
coefficient_list = []
for obj in standards_set:
input_db = os.path.join(directory, obj+".db")
airmasses, magnitudes, filters, SNR = extract.main(input_db)
magnitude_errors_minus, magnitude_errors_plus = snr.snr_to_error(SNR)
mag_errors = (-magnitude_errors_minus + magnitude_errors_plus) / 2.
for filt in set(filters.flatten()):
# find columns with the current filter
