def parallel_sextractor(args):
""" Run SExtractor and compute the FWHM and elongation of a FITS image.
This method is intended to be used with a multiprocessing' pool of workers.
It receives a two-element tuple, the path of an image and the 'instance'
object returned by optparse's parse_args(), containing the values for all
the options of the program, and runs SExtractor on the image. The median or
mean (depending whether the --mean option was given) FWHM and elongation of
the stars in the image is also computed. Nothing is returned; instead, the
result is saved to the global variable 'queue' as a five-element tuple: (1)
path of the input image, (2) path of the temporary output image (a copy of
the input image, with the path to the SExtractor catalog and the MD5 hash
of the configuration files written to its header), (3) FWHM, (4) elongation
and (5) number of objects that were detected by SExtractor. Nothing is
added to 'queue' in case an error is encountered.
"""
path, options = args
mode = options.mean and 'mean' or 'median'
try:
# FITSeeingImage.__init__() writes to the header of the FITS image the
# path to the SExtractor catalog and the MD5 hash of the configuration
# files. Work on a copy of the input image so as not to modify it.
kwargs = dict(prefix = '%s_' % os.path.basename(path),
suffix = '.fits')
fd, output_path = tempfile.mkstemp(**kwargs)
os.close(fd)
shutil.copy2(path, output_path)
# Allow FITSeeingImage.__init__() to write to the FITS header
methods.owner_writable(output_path, True) # chmod u+w
args = output_path, options.maximum, options.margin
kwargs = dict(coaddk = options.coaddk)
image = FITSeeingImage(*args, **kwargs)
fwhm = image.fwhm(per = options.per, mode = mode)
logging.debug("%s: FWHM = %.3f" % (path, fwhm))
elong = image.elongation(per = options.per, mode = mode)
logging.debug("%s: Elongation = %.3f" % (path, elong))
nstars = len(image)
logging.debug("%s: %d sources detected" % (path, nstars))
queue.put((path, output_path, fwhm, elong, nstars))
except fitsimage.NonStandardFITS:
logging.info("%s ignored (non-standard FITS)" % path)
return
# Raised if no star can be used to compute the FWHM or elongation
except ValueError, e:
logging.info("%s ignored (%s)" % (path, str(e)))
def parallel_sextractor(args):
""" Run SExtractor and compute the FWHM and elongation of a FITS image.
This method is intended to be used with a multiprocessing' pool of workers.
It receives a two-element tuple, the path of an image and the 'instance'
object returned by optparse's parse_args(), containing the values for all
the options of the program, and runs SExtractor on the image. The median or
mean (depending whether the --mean option was given) FWHM and elongation of
the stars in the image is also computed. Nothing is returned; instead, the
result is saved to the global variable 'queue' as a five-element tuple: (1)
path of the input image, (2) path of the temporary output image (a copy of
the input image, with the path to the SExtractor catalog and the MD5 hash
of the configuration files written to its header), (3) FWHM, (4) elongation
and (5) number of objects that were detected by SExtractor. Nothing is
added to 'queue' in case an error is encountered.
"""
path, options = args
mode = options.mean and 'mean' or 'median'
try:
# FITSeeingImage.__init__() writes to the header of the FITS image the
# path to the SExtractor catalog and the MD5 hash of the configuration
# files. Work on a copy of the input image so as not to modify it.
kwargs = dict(prefix='%s_' % os.path.basename(path), suffix='.fits')
fd, output_path = tempfile.mkstemp(**kwargs)
os.close(fd)
shutil.copy2(path, output_path)
# Allow FITSeeingImage.__init__() to write to the FITS header
methods.owner_writable(output_path, True) # chmod u+w
args = output_path, options.maximum, options.margin
kwargs = dict(coaddk=options.coaddk)
image = FITSeeingImage(*args, **kwargs)
fwhm = image.fwhm(per=options.per, mode=mode)
logging.debug("%s: FWHM = %.3f" % (path, fwhm))
elong = image.elongation(per=options.per, mode=mode)
logging.debug("%s: Elongation = %.3f" % (path, elong))
nstars = len(image)
logging.debug("%s: %d sources detected" % (path, nstars))
queue.put((path, output_path, fwhm, elong, nstars))
except fitsimage.NonStandardFITS:
logging.info("%s ignored (non-standard FITS)" % path)
return
# Raised if no star can be used to compute the FWHM or elongation
except ValueError, e:
logging.info("%s ignored (%s)" % (path, str(e)))
def main(arguments = None):
""" main() function, encapsulated in a method to allow for easy invokation.
This method follows Guido van Rossum's suggestions on how to write Python
main() functions in order to make them more flexible. By encapsulating the
main code of the script in a function and making it take an optional
argument the script can be called not only from other modules, but also
from the interactive Python prompt.
Guido van van Rossum - Python main() functions:
http://www.artima.com/weblogs/viewpost.jsp?thread=4829
Keyword arguments:
arguments - the list of command line arguments passed to the script.
"""
if arguments is None:
arguments = sys.argv[1:] # ignore argv[0], the script name
(options, args) = parser.parse_args(args = arguments)
# Adjust the logger level to WARNING, INFO or DEBUG, depending on the
# given number of -v options (none, one or two or more, respectively)
logging_level = logging.WARNING
if options.verbose == 1:
logging_level = logging.INFO
elif options.verbose >= 2:
logging_level = logging.DEBUG
logging.basicConfig(format = style.LOG_FORMAT, level = logging_level)
if len(args) != 2:
parser.print_help()
return 2 # used for command line syntax errors
else:
assert len(args) == 2
input_db_path = args[0]
output_db_path = args[1]
if options.min_cstars > options.ncstars:
print "%sError. The value of --minimum-stars must be <= --stars." % style.prefix
print style.error_exit_message
return 1
if not os.path.exists(input_db_path):
print "%sError. Database '%s' does not exist." % (style.prefix, input_db_path)
print style.error_exit_message
return 1
if os.path.exists(output_db_path):
if not options.overwrite:
print "%sError. The output database '%s' already exists." % \
(style.prefix, output_db_path)
print style.error_exit_message
return 1
else:
os.unlink(output_db_path)
# Note that we do not allow the user to update an existing LEMON database,
# although it would make sense if we had just done photometry and now we
# wanted to generate the light curves. However, and to be on the safe side,
# we prefer to preserve a copy of the original database, as it is not
# inconceivable that the astronomer may need to recompute the curves more
# than once, each time with a different set of parameters.
print "%sMaking a copy of the input database..." % style.prefix ,
sys.stdout.flush()
shutil.copy2(input_db_path, output_db_path)
methods.owner_writable(output_db_path, True) # chmod u+w
print 'done.'
db = database.LEMONdB(output_db_path);
nstars = len(db)
print "%sThere are %d stars in the database" % (style.prefix, nstars)
for pfilter in sorted(db.pfilters):
print style.prefix
print "%sLight curves for the %s filter will now be generated." % \
(style.prefix, pfilter)
print "%sLoading photometric information..." % style.prefix ,
sys.stdout.flush()
all_stars = [db.get_photometry(star_id, pfilter) for star_id in db.star_ids]
print 'done.'
# The generation of each light curve is a task independent from the
# others, so we can use a pool of workers and do it in parallel.
pool = multiprocessing.Pool(options.ncores)
map_async_args = ((star, all_stars, options) for star in all_stars)
result = pool.map_async(parallel_light_curves, map_async_args)
methods.show_progress(0.0)
while not result.ready():
time.sleep(1)
methods.show_progress(queue.qsize() / len(all_stars) * 100)
# Do not update the progress bar when debugging; instead, print it
# on a new line each time. This prevents the next logging message,
# if any, from being printed on the same line that the bar.
if logging_level < logging.WARNING:
print
result.get() # reraise exceptions of the remote call, if any
methods.show_progress(100) # in case the queue was ready too soon
print
# The multiprocessing queue contains two-element tuples,
# mapping the ID of each star to its light curve.
print "%sStoring the light curves in the database..." % style.prefix
methods.show_progress(0)
light_curves = (queue.get() for x in xrange(queue.qsize()))
for index, (star_id, curve) in enumerate(light_curves):
# NoneType is returned by parallel_light_curves when the light
# curve could not be calculated -- because it did not meet the
# minimum number of images or comparison stars.
if curve is None:
logging.debug("Nothing for star %d; light curve could not "
"be generated" % star_id)
continue
logging.debug("Storing light curve for star %d in database" % star_id)
db.add_light_curve(star_id, curve)
logging.debug("Light curve for star %d successfully stored" % star_id)
methods.show_progress(100 * (index + 1) / len(all_stars))
if logging_level < logging.WARNING:
print
else:
logging.info("Light curves for %s generated" % pfilter)
logging.debug("Committing database transaction")
db.commit()
logging.info("Database transaction commited")
methods.show_progress(100.0)
print
print "%sUpdating statistics about tables and indexes..." % style.prefix ,
sys.stdout.flush()
db.analyze()
print 'done.'
# Update LEMONdB metadata
db.date = time.time()
db.author = pwd.getpwuid(os.getuid())[0]
db.hostname = socket.gethostname()
db.commit()
methods.owner_writable(output_db_path, False) # chmod u-w
print "%sYou're done ^_^" % style.prefix
return 0
def main(arguments=None):
""" main() function, encapsulated in a method to allow for easy invokation.
This method follows Guido van Rossum's suggestions on how to write Python
main() functions in order to make them more flexible. By encapsulating the
main code of the script in a function and making it take an optional
argument the script can be called not only from other modules, but also
from the interactive Python prompt.
Guido van van Rossum - Python main() functions:
http://www.artima.com/weblogs/viewpost.jsp?thread=4829
Keyword arguments:
arguments - the list of command line arguments passed to the script.
"""
if arguments is None:
arguments = sys.argv[1:] # ignore argv[0], the script name
(options, args) = parser.parse_args(args=arguments)
# Adjust the logger level to WARNING, INFO or DEBUG, depending on the
# given number of -v options (none, one or two or more, respectively)
logging_level = logging.WARNING
if options.verbose == 1:
logging_level = logging.INFO
elif options.verbose >= 2:
logging_level = logging.DEBUG
logging.basicConfig(format=style.LOG_FORMAT, level=logging_level)
if len(args) != 2:
parser.print_help()
return 2 # used for command line syntax errors
else:
assert len(args) == 2
input_db_path = args[0]
output_db_path = args[1]
if options.min_cstars > options.ncstars:
print "%sError. The value of --minimum-stars must be <= --stars." % style.prefix
print style.error_exit_message
return 1
if not os.path.exists(input_db_path):
print "%sError. Database '%s' does not exist." % (style.prefix,
input_db_path)
print style.error_exit_message
return 1
if os.path.exists(output_db_path):
if not options.overwrite:
print "%sError. The output database '%s' already exists." % \
(style.prefix, output_db_path)
print style.error_exit_message
return 1
else:
os.unlink(output_db_path)
# Note that we do not allow the user to update an existing LEMON database,
# although it would make sense if we had just done photometry and now we
# wanted to generate the light curves. However, and to be on the safe side,
# we prefer to preserve a copy of the original database, as it is not
# inconceivable that the astronomer may need to recompute the curves more
# than once, each time with a different set of parameters.
print "%sMaking a copy of the input database..." % style.prefix,
sys.stdout.flush()
shutil.copy2(input_db_path, output_db_path)
methods.owner_writable(output_db_path, True) # chmod u+w
print 'done.'
db = database.LEMONdB(output_db_path)
nstars = len(db)
print "%sThere are %d stars in the database" % (style.prefix, nstars)
for pfilter in sorted(db.pfilters):
print style.prefix
print "%sLight curves for the %s filter will now be generated." % \
(style.prefix, pfilter)
print "%sLoading photometric information..." % style.prefix,
sys.stdout.flush()
all_stars = [
db.get_photometry(star_id, pfilter) for star_id in db.star_ids
]
print 'done.'
# The generation of each light curve is a task independent from the
# others, so we can use a pool of workers and do it in parallel.
pool = multiprocessing.Pool(options.ncores)
map_async_args = ((star, all_stars, options) for star in all_stars)
result = pool.map_async(parallel_light_curves, map_async_args)
methods.show_progress(0.0)
while not result.ready():
time.sleep(1)
methods.show_progress(queue.qsize() / len(all_stars) * 100)
# Do not update the progress bar when debugging; instead, print it
# on a new line each time. This prevents the next logging message,
# if any, from being printed on the same line that the bar.
if logging_level < logging.WARNING:
print
result.get() # reraise exceptions of the remote call, if any
methods.show_progress(100) # in case the queue was ready too soon
print
# The multiprocessing queue contains two-element tuples,
# mapping the ID of each star to its light curve.
print "%sStoring the light curves in the database..." % style.prefix
methods.show_progress(0)
light_curves = (queue.get() for x in xrange(queue.qsize()))
for index, (star_id, curve) in enumerate(light_curves):
# NoneType is returned by parallel_light_curves when the light
# curve could not be calculated -- because it did not meet the
# minimum number of images or comparison stars.
if curve is None:
logging.debug("Nothing for star %d; light curve could not "
"be generated" % star_id)
continue
logging.debug("Storing light curve for star %d in database" %
star_id)
db.add_light_curve(star_id, curve)
logging.debug("Light curve for star %d successfully stored" %
star_id)
methods.show_progress(100 * (index + 1) / len(all_stars))
if logging_level < logging.WARNING:
print
else:
logging.info("Light curves for %s generated" % pfilter)
logging.debug("Committing database transaction")
db.commit()
logging.info("Database transaction commited")
methods.show_progress(100.0)
print
print "%sUpdating statistics about tables and indexes..." % style.prefix,
sys.stdout.flush()
db.analyze()
print 'done.'
# Update LEMONdB metadata
db.date = time.time()
db.author = pwd.getpwuid(os.getuid())[0]
db.hostname = socket.gethostname()
db.commit()
methods.owner_writable(output_db_path, False) # chmod u-w
print "%sYou're done ^_^" % style.prefix
return 0
def main(arguments = None):
""" main() function, encapsulated in a method to allow for easy invokation.
This method follows Guido van Rossum's suggestions on how to write Python
main() functions in order to make them more flexible. By encapsulating the
main code of the script in a function and making it take an optional
argument the script can be called not only from other modules, but also
from the interactive Python prompt.
Guido van van Rossum - Python main() functions:
http://www.artima.com/weblogs/viewpost.jsp?thread=4829
Keyword arguments:
arguments - the list of command line arguments passed to the script.
"""
if arguments is None:
arguments = sys.argv[1:] # ignore argv[0], the script name
(options, args) = parser.parse_args(args = arguments)
# Print the help message and abort the execution if there are not two
# positional arguments left after parsing the options, as the user must
# specify the path to both the input and output directories.
if len(args) < 2:
parser.print_help()
return 2 # 2 is generally used for command line syntax errors
else:
input_dirs = args[:-1]
output_dir = args[-1]
# Make sure that all the input directories exist, abort otherwise.
for path in input_dirs:
if not os.path.exists(path):
print "%sThe input directory, '%s', does not exist. Exiting." % \
(style.prefix, path)
return 1
# The input and output directories must be different, as otherwise some
# files (especially if the filename of the output files is automatically
# detected) could be overwritten.
for path in input_dirs:
if os.path.abspath(path) == os.path.abspath(output_dir):
print "%s[INPUT_DIRS] and OUTPUT_DIR must be different. " \
"Exiting." % style.prefix
return 1
# Make sure that the output directory exists, create it otherwise
methods.determine_output_dir(output_dir)
# Recursively walk down the input directories, obtaining a list of all the
# regular files. Then, and while a progress bar is shown to let the user
# estimate how much longer it is, detect which among them are FITS files.
print "%sIndexing regular files within directory trees starting at " \
"INPUT_DIRS..." % style.prefix ,
files_paths = fitsimage.find_files(input_dirs,
followlinks = options.followlinks,
pattern = options.pattern)
print 'done.'
print "%sDetecting FITS images among the %d indexed regular files..." % \
(style.prefix, len(files_paths))
images_set = set()
methods.show_progress(0.0)
for path_index, path in enumerate(files_paths):
try:
images_set.add(fitsimage.FITSImage(path))
fraction = (path_index + 1) / len(files_paths) * 100
methods.show_progress(fraction)
except fitsimage.NonStandardFITS:
pass
else:
methods.show_progress(100)
print
if not len(images_set):
print "%sNo FITS files were found. Exiting." % style.prefix
return 1
else:
print "%s%d FITS files detected." % (style.prefix, len(images_set))
# All the images must have the same size; otherwise, only those with the
# most common dimensions will be imported, while the rest will be ignored
print style.prefix
print "%sChecking the sizes of the detected images..." % style.prefix,
img_sizes = collections.defaultdict(int) # dimensions counter
for img in images_set:
img_sizes[img.size] += 1
print 'done.'
# The most common size is the only one element in case len(img_sizes) == 1
x_size, y_size = max(img_sizes.iterkeys(), key = img_sizes.get)[:2]
if len(img_sizes) == 1:
print "%sAll the FITS images have the same size: %d x %d pixels" % \
(style.prefix, x_size, y_size)
else:
print "%sMultiple sizes were detected among the FITS images." % style.prefix
print "%sDiscarding images with a size other than %d x %d pixels, " \
"the most common..." % (style.prefix, x_size, y_size) ,
old_size = len(images_set)
images_set = set(img for img in images_set if img.size == (x_size, y_size))
print 'done.'
if not images_set:
print "%sThere are no FITS files left. Exiting." % style.prefix
return 1
else:
print "%s%d FITS files were discarded because of their size, " \
"%s remain." % (style.prefix, old_size - len(images_set),
len(images_set))
# Those FITS images whose object names do not match any of the given
# patterns, or which do not even have the keyword which contains the
# name for the object observed, are discarded.
print style.prefix
print "%sImporting only those FITS files whose %s keyword can be found " \
"and matches" % (style.prefix, options.objectk)
print "%sone of the following Unix patterns: %s ..." % \
(style.prefix, options.objectn)
# We first test that the keyword exists (hence the pass for the KeyError
# exception, which means that the image is filtered out) and, after that,
# check whether its value matches one of the regular expressions which
# define the object names to be imported.
object_set = set()
# Keep the track of how many images are ignored for each reason
saturated_excluded = 0
non_match_excluded = 0
for img in images_set:
try:
object_name = img.read_keyword(options.objectk)
for pattern in options.objectn:
regexp = re.compile(fnmatch.translate(pattern), re.IGNORECASE)
if regexp.match(object_name):
# Even if the object name matchs, the median number of
# counts must still be below the threshold, if any. If the
# number of ADUs is irrelevant we can avoid having to
# unnecessarily compute it.
if options.max_counts:
with pyfits.open(img.path, readonly = True) as hdu:
median_counts = numpy.median(hdu[0].data)
if median_counts > options.max_counts:
print "%s%s excluded (matched, but saturated " \
"with %d ADUs)" % (style.prefix, img.path,
median_counts)
saturated_excluded += 1
break
# This point reached if median number of ADUs of image is
# above the threshold or irrelevant, so it can be imported.
print "%s%s imported (%s matches '%s')" % (style.prefix,
img.path, object_name, pattern)
object_set.add(img)
break
else: # only executed if for loop exited cleanly
print "%s%s excluded (%s does not match anything)" % \
(style.prefix, img.path, object_name)
non_match_excluded += 1
except KeyError:
pass
if not saturated_excluded and not non_match_excluded:
print "%sNo images were filtered out. Hooray!" % style.prefix
if saturated_excluded:
print "%s%d files were discarded because they were saturated " \
"(> %d ADUs)." % (style.prefix, saturated_excluded,
options.max_counts)
if non_match_excluded:
print "%s%d files were discarded because of their non-matching " \
"object names." % (style.prefix, non_match_excluded)
# Abort the execution if all the FITS files were filtered out
if not object_set:
print "%sThere are no FITS files left. Exiting." % style.prefix
return 1
# Sort the FITS files by their date of observation, according to the header
print style.prefix
print "%sSorting the FITS files by their date of observation " \
"[keyword: %s]..." % (style.prefix, options.datek) ,
kwargs = dict(date_keyword = options.datek,
time_keyword = options.timek,
exp_keyword = options.exptimek)
get_date = operator.methodcaller('date', **kwargs)
sorted_imgs = sorted(object_set, key = get_date)
# Let the user know if one or more images could not be sorted (because of
# problems when parsing the FITS keywords from which the observation date
# is derived) and thus discarded.
difference = len(object_set) - len(sorted_imgs)
assert difference >= 0
if difference:
print
print "%s%d files were discarded as the observation date keyword " \
"was not found or the " % (style.prefix, difference)
print "%sdate in it represented did not conform to the FITS " \
"standard." % style.prefix
# Execution is aborted if all the FITS files were filtered out
if not sorted_imgs:
print "%sThere are no FITS files left. Exiting." % style.prefix
return 1
else:
print 'done.'
# If no filename for the output images was specified, attempt to
# automatically detect the most common basename among the FITS files.
# This is doing by extracting the leftmost non-numeric substring of
# all the filenames and taking that which repeats the most.
if not options.filename:
print style.prefix
print "%sDetecting the most common name among input files..." % \
style.prefix ,
sys.stdout.flush()
# Use a dictionary in order to keep the track of how many times we
# have come across each prefix (leftmost non-numeric substring in
# the filename) and select that with most occurrences.
prefixes = collections.defaultdict(int)
for prefix in (img.prefix for img in sorted_imgs):
prefixes[prefix] += 1
# Select the prefix (key) that is repeated the most
options.filename = max(prefixes, key = prefixes.get)
print 'done.'
print "%sImported FITS filenames will start with the string: '%s'" % \
(style.prefix, options.filename)
# Now we have to copy the FITS files. The basename of each imported file
# will be options.filename + its sequence number. Filling zeros will be
# affixed to each number so that the lenth of all the basenames is the
# same. Following Dijkstra's teachings, we start numbering at zero.
assert len(sorted_imgs)
ndigits = len(str(len(sorted_imgs) - 1))
print "%s%d digits are needed in order to enumerate %d files." % \
(style.prefix, ndigits, len(sorted_imgs))
print style.prefix
print "%sCopying the FITS files to '%s'..." % \
(style.prefix, output_dir)
for index, fits_file in enumerate(sorted_imgs):
# i.e., 'ferM_' + '0000' + '.fits' = 'ferM_0000.fits'
dest_name = '%s%0*d.fits' % (options.filename, ndigits, index)
dest_path = os.path.join(output_dir, dest_name)
shutil.copy2(fits_file.path, dest_path)
# The permission bits have been copied, but we need to make sure
# that the copy of the FITS file is always writable, no matter what
# the original permissions were. This is equivalent to `chmod u+w`
methods.owner_writable(dest_path, True)
dest_img = fitsimage.FITSImage(dest_path)
# Add some information to the FITS header...
if not options.exact:
msg1 = "File imported by LEMON on %s" % methods.utctime()
dest_img.add_history(msg1)
# If the --uik option is given, store in this keyword the absolute
# path to the image of which we made a copy. This allows other
# LEMON commands, if necessary, to access the original FITS files
# in case the imported images are modified (e.g., bias subtraction
# or flat-fielding) before these other commands are executed.
if options.uncimgk:
comment = "before any calibration task"
dest_img.update_keyword(options.uncimgk,
os.path.abspath(dest_img.path),
comment = comment)
msg2 = "[Import] Original image: %s"
dest_img.add_history(msg2 % os.path.abspath(fits_file.path))
# ... unless we want an exact copy of the images. If that is the case,
# verify that the SHA-1 checksum of the original and the copy matches
elif fits_file.sha1sum != dest_img.sha1sum:
msg = "copy of %s not identical (SHA-1 differs)" % fits_file.path
raise IOError(msg)
# Show which file has been copied, using the format of the
# 'cp -v' command: `./ultra2/ferM_11.fits' -> `imported/img_01.fits'
print "%s`%s' -> `%s'" % (style.prefix, fits_file.path, dest_path)
# Finally, let the user know how many FITS images, and the fraction of
# the total, that were imported, as well as their size in megabytes.
print style.prefix
ifraction = len(sorted_imgs) / len(images_set) * 100
print "%sFITS files detected: %d" % (style.prefix, len(images_set))
print "%sFITS files successfully imported: %d (%.2f%%)" % \
(style.prefix, len(sorted_imgs), ifraction)
total_size = 0.0
for fits_file in sorted_imgs:
total_size += os.path.getsize(fits_file.path) # in bytes
print "%sTotal size of imported files: %.2f MB" % \
(style.prefix, total_size / (1024.0 ** 2))
print "%sYou're done ^_^" % style.prefix
return 0
def main(arguments = None):
""" main() function, encapsulated in a method to allow for easy invokation.
This method follows Guido van Rossum's suggestions on how to write Python
main() functions in order to make them more flexible. By encapsulating the
main code of the script in a function and making it take an optional
argument the script can be called not only from other modules, but also
from the interactive Python prompt.
Guido van van Rossum - Python main() functions:
http://www.artima.com/weblogs/viewpost.jsp?thread=4829
Keyword arguments:
arguments - the list of command line arguments passed to the script.
"""
if arguments is None:
arguments = sys.argv[1:] # ignore argv[0], the script name
(options, args) = parser.parse_args(args = arguments)
# Adjust the logger level to WARNING, INFO or DEBUG, depending on the
# given number of -v options (none, one or two or more, respectively)
logging_level = logging.WARNING
if options.verbose == 1:
logging_level = logging.INFO
elif options.verbose >= 2:
logging_level = logging.DEBUG
logging.basicConfig(format = style.LOG_FORMAT, level = logging_level)
# Print the help and abort the execution if there are not two positional
# arguments left after parsing the options, as the user must specify at
# least one (only one?) input FITS file and the output directory
if len(args) < 2:
parser.print_help()
return 2 # 2 is generally used for command line syntax errors
else:
input_paths = args[:-1]
output_dir = args[-1]
# Make sure that the output directory exists, and create it if it doesn't.
# The subdirectories for discarded images are not yet created; we put this
# off until we know that at least one image is indeed going to be excluded.
methods.determine_output_dir(output_dir)
fwhm_dir = os.path.join(output_dir, options.fwhm_dir)
elong_dir = os.path.join(output_dir, options.elong_dir)
print "%s%d paths given as input, on which sources will be detected." % \
(style.prefix, len(input_paths))
print "%sRunning SExtractor on all the FITS images..." % style.prefix
# Use a pool of workers and run SExtractor on the images in parallel!
pool = multiprocessing.Pool(options.ncores)
map_async_args = ((path, options) for path in input_paths if os.path.isfile(path))
result = pool.map_async(parallel_sextractor, map_async_args)
methods.show_progress(0.0)
while not result.ready():
time.sleep(1)
methods.show_progress(queue.qsize() / len(input_paths) * 100)
# Do not update the progress bar when debugging; instead, print it
# on a new line each time. This prevents the next logging message,
# if any, from being printed on the same line that the bar.
if logging_level < logging.WARNING:
print
result.get() # reraise exceptions of the remote call, if any
methods.show_progress(100) # in case the queue was ready too soon
print
# Three sets, to keep the track of all the images on which SExtractor
# has been run and also of which have been discarded because of their
# unnaceptable FWHM or elongation ratio.
all_images = set()
fwhm_discarded = set()
elong_discarded = set()
# Dictionary mapping each input image to the temporary output file: a copy
# of the input image but whose FITS header has been updated with the path
# to the SExtractor catalog and the MD5 hash of the configuration files.
seeing_tmp_paths = dict()
# Extract the four-element tuples (path to the image, FWHM, elongation and
# number of sources detected by SExtractor) from the multiprocessing' queue
# and store the values in three independent dictionaries; these provide
# fast access, with O(1) lookup, to the data.
fwhms = {}
elongs = {}
nstars = {}
for _ in xrange(queue.qsize()):
path, output_tmp_path, fwhm, elong, stars = queue.get()
all_images.add(path)
seeing_tmp_paths[path] = output_tmp_path
# The clean-up function cannot be registered in parallel_sextractor()
# because it would remove the temporary FITS file when the process
# terminates (instead of when our program exits, which is what we
# need). Do it here, to make sure that whatever happens next these
# temporary files are always deleted.
atexit.register(methods.clean_tmp_files, output_tmp_path)
fwhms[path] = fwhm
elongs[path] = elong
nstars[path] = stars
if not all_images:
print "%sError. No FITS images were detected." % style.prefix
print style.error_exit_message
return 1
# Let's first discard those images with a bad full width at half maximum.
# In order to to this, we fit a normal distribution (assuming the FWHMs to
# be Gaussian distributed) and define the maximum allowed value as that
# which exceeds the specified number of standard deviations of the mean.
print "%sFitting a Gaussian distribution to the FWHMs..." % style.prefix ,
sys.stdout.flush()
logging.debug("Fitting a Gaussian distribution to the %d FWHMs" % len(fwhms))
mu, sigma = scipy.stats.norm.fit(fwhms.values())
logging.debug("FWHMs mean = %.3f" % mu)
logging.debug("FWHMs sigma = %.3f" % sigma)
print 'done.'
sys.stdout.flush()
print "%sFWHMs mean = %.3f, sigma = %.3f pixels" % (style.prefix, mu, sigma)
maximum_fwhm = mu + (options.fwhm_sigma * sigma)
logging.debug("Maximum allowed FWHM = %.3f + %.1f x %.3f = %.3f pixels" % \
(mu, options.fwhm_sigma, sigma, maximum_fwhm))
print "%sDiscarding images with a FWHM > %.3f + %.1f x %.3f = %.3f pixels..." % \
(style.prefix, mu, options.fwhm_sigma, sigma, maximum_fwhm)
# Exclude images by adding them to the FWHM-discarded set
for path, fwhm in sorted(fwhms.iteritems()):
if fwhm > maximum_fwhm:
fwhm_discarded.add(path)
logging.debug("%s discarded (FWHM = %.3f > %.3f" % \
(path, fwhm, maximum_fwhm))
print "%s%s discarded (FWHM = %.3f)" % (style.prefix, path, fwhm)
logging.info("Images discarded by FWHM: %d" % len(fwhm_discarded))
if not fwhm_discarded:
print "%sNo images were discarded because of their FWHM. Hooray!" % style.prefix
else:
discarded_fraction = len(fwhm_discarded) / len(all_images) * 100
nleft = len(all_images) - len(fwhm_discarded) # non-discarded images
print "%s%d FITS images (%.2f %%) discarded, %d remain" % \
(style.prefix, len(fwhm_discarded), discarded_fraction, nleft)
# Repeat the same approach, now with the elongation ratios. Images already
# discarded because of their FWHM are not even considered -- why discard
# them twice? They can simply be ignored.
print "%sFitting a Gaussian distribution to the elongations..." % style.prefix ,
sys.stdout.flush()
mu, sigma = scipy.stats.norm.fit(elongs.values())
logging.debug("Elongations mean = %.3f" % mu)
logging.debug("Elongations sigma = %.3f" % sigma)
print 'done.'
sys.stdout.flush()
print "%sElongation mean = %.3f, sigma = %.3f pixels" % (style.prefix, mu, sigma)
maximum_elong = mu + (options.elong_sigma * sigma)
logging.debug("Maximum allowed elongation = %.3f + %.1f x %.3f = %.3f pixels" % \
(mu, options.elong_sigma, sigma, maximum_elong))
print "%sDiscarding images with an elongation > %.3f + %.1f x %.3f = %.3f ..." % \
(style.prefix, mu, options.elong_sigma, sigma, maximum_elong)
for path, elong in sorted(elongs.iteritems()):
# Ignore FWHM-discarded images
if path in fwhm_discarded:
logging.debug("%s ignored (already discarded by FWHM)" % path)
continue
elif elong > maximum_elong:
elong_discarded.add(path)
logging.debug("%s discarded (elongation = %.3f > %.3f" % \
(path, fwhm, maximum_elong))
print "%s%s discarded (elongation = %.3f)" % (style.prefix, path, elong)
logging.info("Images discarded by elongation: %d" % len(elong_discarded))
if not elong_discarded:
print "%sNo images were discarded because of their elongation. Yay!" % style.prefix
else:
initial_size = len(all_images) - len(fwhm_discarded)
discarded_fraction = len(elong_discarded) / initial_size * 100
nleft = initial_size - len(elong_discarded)
print "%s%d FITS images (%.2f %%) discarded, %d remain" % \
(style.prefix, len(elong_discarded), discarded_fraction, nleft)
# Finally, take the images whose number of stars is at the 'stars_per'
# percentile and select the one with the best FWHM. This will be our
# 'best-seeing' image, in which sources may be detected. Taking directly
# the image with the best FWHM may not work as we need the best-seeomg
# image to also be one of the most populated.
print "%sIdentifying the images whose number of detected sources it at " \
"the %.2f percentile..." % (style.prefix, options.stars_per) ,
sys.stdout.flush()
# Ignore discarded images, for whatever reason
logging.debug("Finding the %.2f percentile of the number of stars " \
"detected by SExtractor" % options.stars_per)
for path in fwhm_discarded.union(elong_discarded):
del nstars[path]
reason = 'FWHM' if path in fwhm_discarded else 'elongation'
logging.debug("%s ignored (was discarded by %s)" % (path, reason))
min_nstars = scipy.stats.scoreatpercentile(nstars.values(), options.stars_per)
print 'done.'
print "%sNumber of stars at percentile = %d, taking the images with at " \
"least this number of sources..." % (style.prefix, min_nstars) ,
sys.stdout.flush()
most_populated_images = [path
for path, stars in nstars.iteritems()
if stars >= min_nstars]
logging.debug("There are %s images with a number of stars at the %.2f " \
"percentile" % (len(most_populated_images), options.stars_per))
logging.debug("Identifying the image with the lowest FWHM")
print 'done.'
print "%sFinally, finding the image with the lowest FWHM among these " \
"%d images..." % (style.prefix, len(most_populated_images)),
sys.stdout.flush()
# Find the image with the best seeing (lowest FWHM)
best_seeing = min(most_populated_images, key = lambda path: fwhms[path])
logging.debug("Best-seeing image: %s" % path)
logging.debug("Best-seeing image FWHM = %.3f" % fwhms[best_seeing])
logging.debug("Best-seeing image elongation = %.3f" % elongs[best_seeing])
logging.debug("Best-seeing image sources = %d" % nstars[best_seeing])
assert best_seeing not in fwhm_discarded
assert best_seeing not in elong_discarded
print 'done.'
print "%sBest-seeing image = %s, with %d sources and a FWHM of %.3f pixels" % \
(style.prefix, best_seeing, nstars[best_seeing], fwhms[best_seeing])
# The subdirectories are created only if at least one image is going to be
# discarded. We do not want empty directories in case no image is discarded
# because of its full-width at half maximum (FWHM) or elongation.
if fwhm_discarded:
methods.determine_output_dir(fwhm_dir, quiet = True)
if elong_discarded:
methods.determine_output_dir(elong_dir, quiet = True)
# Finally, copy all the FITS images to the output directory
processed = 0
for path in sorted(all_images):
# Add the suffix to the basename of the FITS image
root, ext = os.path.splitext(os.path.basename(path))
output_filename = root + options.suffix + ext
logging.debug("Basename '%s' + '%s' becomes '%s'" % \
(path, options.suffix, output_filename))
if path in fwhm_discarded:
output_path = os.path.join(fwhm_dir, output_filename)
logging.debug("%s was discarded because of its FWHM" % path)
logging.debug("%s to be copied to subdirectory %s" % (path, fwhm_dir))
history_msg1 = "Image discarded by LEMON on %s" % methods.utctime()
history_msg2 = "[Discarded] FWHM = %.3f pixels, maximum allowed value = %.3f" % \
(fwhms[path], maximum_fwhm)
elif path in elong_discarded:
output_path = os.path.join(elong_dir, output_filename)
logging.debug("%s was discarded because of its elongation ratio" % path)
logging.debug("%s to be copied to subdirectory %s" % (path, elong_dir))
history_msg1 = "Image discarded by LEMON on %s" % methods.utctime()
history_msg2 = "[Discarded] Elongation = %.3f, maximum allowed value = %.3f" % \
(elongs[path], maximum_elong)
elif path == best_seeing:
# Retain original name if --filename is an empty string
if not options.bseeingfn:
filename = output_filename
else:
filename = options.bseeingfn
output_path = os.path.join(output_dir, filename)
logging.debug("%s is the best-seeing image" % path)
logging.debug("%s to be copied to directory %s with name %s" % \
(path, output_dir, options.bseeingfn))
history_msg1 = "Image identified by LEMON as the 'best-seeing' one"
history_msg2 = "FWHM = %.3f | Elongation = %.3f | Sources: %d (at %.2f percentile)" % \
(fwhms[path], elongs[path], nstars[path], options.stars_per)
else:
output_path = os.path.join(output_dir, output_filename)
logging.debug("%s to be copied to %s" % (path, output_dir))
history_msg1 = "Image FWHM = %.3f" % fwhms[path]
history_msg2 = "Image elongation = %.3f" % elongs[path]
if os.path.exists(output_path) and not options.overwrite:
msg = ("%sError. Output FITS file '%s' already exists. "
"You need to use --overwrite.")
args = style.prefix, output_path
print msg % args
print style.error_exit_message
return 1
else:
src = seeing_tmp_paths[path]
shutil.move(src, output_path)
methods.owner_writable(output_path, True) # chmod u+w
logging.debug("%s copied to %s" % (path, output_path))
output_img = fitsimage.FITSImage(output_path)
output_img.add_history(history_msg1)
output_img.add_history(history_msg2)
logging.debug("%s: FITS header updated (HISTORY keywords)" % path)
# Copy the FWHM to the FITS header, for future reference
comment = "Margin = %d, SNR percentile = %.3f" % (options.margin, options.per)
output_img.update_keyword(options.fwhmk, fwhms[path], comment = comment)
logging.debug("%s: FITS header updated (%s keyword)" % (path, options.fwhmk))
print "%sFITS image %s saved to %s" % (style.prefix, path, output_path)
processed += 1
print "%sA total of %d images was saved to directory '%s'." % (style.prefix, processed, output_dir)
print "%sWe're done ^_^" % style.prefix
return 0
def main(arguments=None):
""" main() function, encapsulated in a method to allow for easy invokation.
This method follows Guido van Rossum's suggestions on how to write Python
main() functions in order to make them more flexible. By encapsulating the
main code of the script in a function and making it take an optional
argument the script can be called not only from other modules, but also
from the interactive Python prompt.
Guido van van Rossum - Python main() functions:
http://www.artima.com/weblogs/viewpost.jsp?thread=4829
Keyword arguments:
arguments - the list of command line arguments passed to the script.
"""
if arguments is None:
arguments = sys.argv[1:] # ignore argv[0], the script name
(options, args) = parser.parse_args(args=arguments)
# Adjust the logger level to WARNING, INFO or DEBUG, depending on the
# given number of -v options (none, one or two or more, respectively)
logging_level = logging.WARNING
if options.verbose == 1:
logging_level = logging.INFO
elif options.verbose >= 2:
logging_level = logging.DEBUG
logging.basicConfig(format=style.LOG_FORMAT, level=logging_level)
# Print the help and abort the execution if there are not two positional
# arguments left after parsing the options, as the user must specify at
# least one (only one?) input FITS file and the output directory
if len(args) < 2:
parser.print_help()
return 2 # 2 is generally used for command line syntax errors
else:
input_paths = args[:-1]
output_dir = args[-1]
# Make sure that the output directory exists, and create it if it doesn't.
# The subdirectories for discarded images are not yet created; we put this
# off until we know that at least one image is indeed going to be excluded.
methods.determine_output_dir(output_dir)
fwhm_dir = os.path.join(output_dir, options.fwhm_dir)
elong_dir = os.path.join(output_dir, options.elong_dir)
print "%s%d paths given as input, on which sources will be detected." % \
(style.prefix, len(input_paths))
print "%sRunning SExtractor on all the FITS images..." % style.prefix
# Use a pool of workers and run SExtractor on the images in parallel!
pool = multiprocessing.Pool(options.ncores)
map_async_args = ((path, options) for path in input_paths
if os.path.isfile(path))
result = pool.map_async(parallel_sextractor, map_async_args)
methods.show_progress(0.0)
while not result.ready():
time.sleep(1)
methods.show_progress(queue.qsize() / len(input_paths) * 100)
# Do not update the progress bar when debugging; instead, print it
# on a new line each time. This prevents the next logging message,
# if any, from being printed on the same line that the bar.
if logging_level < logging.WARNING:
print
result.get() # reraise exceptions of the remote call, if any
methods.show_progress(100) # in case the queue was ready too soon
print
# Three sets, to keep the track of all the images on which SExtractor
# has been run and also of which have been discarded because of their
# unnaceptable FWHM or elongation ratio.
all_images = set()
fwhm_discarded = set()
elong_discarded = set()
# Dictionary mapping each input image to the temporary output file: a copy
# of the input image but whose FITS header has been updated with the path
# to the SExtractor catalog and the MD5 hash of the configuration files.
seeing_tmp_paths = dict()
# Extract the four-element tuples (path to the image, FWHM, elongation and
# number of sources detected by SExtractor) from the multiprocessing' queue
# and store the values in three independent dictionaries; these provide
# fast access, with O(1) lookup, to the data.
fwhms = {}
elongs = {}
nstars = {}
for _ in xrange(queue.qsize()):
path, output_tmp_path, fwhm, elong, stars = queue.get()
all_images.add(path)
seeing_tmp_paths[path] = output_tmp_path
# The clean-up function cannot be registered in parallel_sextractor()
# because it would remove the temporary FITS file when the process
# terminates (instead of when our program exits, which is what we
# need). Do it here, to make sure that whatever happens next these
# temporary files are always deleted.
atexit.register(methods.clean_tmp_files, output_tmp_path)
fwhms[path] = fwhm
elongs[path] = elong
nstars[path] = stars
if not all_images:
print "%sError. No FITS images were detected." % style.prefix
print style.error_exit_message
return 1
# Let's first discard those images with a bad full width at half maximum.
# In order to to this, we fit a normal distribution (assuming the FWHMs to
# be Gaussian distributed) and define the maximum allowed value as that
# which exceeds the specified number of standard deviations of the mean.
print "%sFitting a Gaussian distribution to the FWHMs..." % style.prefix,
sys.stdout.flush()
logging.debug("Fitting a Gaussian distribution to the %d FWHMs" %
len(fwhms))
mu, sigma = scipy.stats.norm.fit(fwhms.values())
logging.debug("FWHMs mean = %.3f" % mu)
logging.debug("FWHMs sigma = %.3f" % sigma)
print 'done.'
sys.stdout.flush()
print "%sFWHMs mean = %.3f, sigma = %.3f pixels" % (style.prefix, mu,
sigma)
maximum_fwhm = mu + (options.fwhm_sigma * sigma)
logging.debug("Maximum allowed FWHM = %.3f + %.1f x %.3f = %.3f pixels" % \
(mu, options.fwhm_sigma, sigma, maximum_fwhm))
print "%sDiscarding images with a FWHM > %.3f + %.1f x %.3f = %.3f pixels..." % \
(style.prefix, mu, options.fwhm_sigma, sigma, maximum_fwhm)
# Exclude images by adding them to the FWHM-discarded set
for path, fwhm in sorted(fwhms.iteritems()):
if fwhm > maximum_fwhm:
fwhm_discarded.add(path)
logging.debug("%s discarded (FWHM = %.3f > %.3f" % \
(path, fwhm, maximum_fwhm))
print "%s%s discarded (FWHM = %.3f)" % (style.prefix, path, fwhm)
logging.info("Images discarded by FWHM: %d" % len(fwhm_discarded))
if not fwhm_discarded:
print "%sNo images were discarded because of their FWHM. Hooray!" % style.prefix
else:
discarded_fraction = len(fwhm_discarded) / len(all_images) * 100
nleft = len(all_images) - len(fwhm_discarded) # non-discarded images
print "%s%d FITS images (%.2f %%) discarded, %d remain" % \
(style.prefix, len(fwhm_discarded), discarded_fraction, nleft)
# Repeat the same approach, now with the elongation ratios. Images already
# discarded because of their FWHM are not even considered -- why discard
# them twice? They can simply be ignored.
print "%sFitting a Gaussian distribution to the elongations..." % style.prefix,
sys.stdout.flush()
mu, sigma = scipy.stats.norm.fit(elongs.values())
logging.debug("Elongations mean = %.3f" % mu)
logging.debug("Elongations sigma = %.3f" % sigma)
print 'done.'
sys.stdout.flush()
print "%sElongation mean = %.3f, sigma = %.3f pixels" % (style.prefix, mu,
sigma)
maximum_elong = mu + (options.elong_sigma * sigma)
logging.debug("Maximum allowed elongation = %.3f + %.1f x %.3f = %.3f pixels" % \
(mu, options.elong_sigma, sigma, maximum_elong))
print "%sDiscarding images with an elongation > %.3f + %.1f x %.3f = %.3f ..." % \
(style.prefix, mu, options.elong_sigma, sigma, maximum_elong)
for path, elong in sorted(elongs.iteritems()):
# Ignore FWHM-discarded images
if path in fwhm_discarded:
logging.debug("%s ignored (already discarded by FWHM)" % path)
continue
elif elong > maximum_elong:
elong_discarded.add(path)
logging.debug("%s discarded (elongation = %.3f > %.3f" % \
(path, fwhm, maximum_elong))
print "%s%s discarded (elongation = %.3f)" % (style.prefix, path,
elong)
logging.info("Images discarded by elongation: %d" % len(elong_discarded))
if not elong_discarded:
print "%sNo images were discarded because of their elongation. Yay!" % style.prefix
else:
initial_size = len(all_images) - len(fwhm_discarded)
discarded_fraction = len(elong_discarded) / initial_size * 100
nleft = initial_size - len(elong_discarded)
print "%s%d FITS images (%.2f %%) discarded, %d remain" % \
(style.prefix, len(elong_discarded), discarded_fraction, nleft)
# Finally, take the images whose number of stars is at the 'stars_per'
# percentile and select the one with the best FWHM. This will be our
# 'best-seeing' image, in which sources may be detected. Taking directly
# the image with the best FWHM may not work as we need the best-seeomg
# image to also be one of the most populated.
print "%sIdentifying the images whose number of detected sources it at " \
"the %.2f percentile..." % (style.prefix, options.stars_per) ,
sys.stdout.flush()
# Ignore discarded images, for whatever reason
logging.debug("Finding the %.2f percentile of the number of stars " \
"detected by SExtractor" % options.stars_per)
for path in fwhm_discarded.union(elong_discarded):
del nstars[path]
reason = 'FWHM' if path in fwhm_discarded else 'elongation'
logging.debug("%s ignored (was discarded by %s)" % (path, reason))
min_nstars = scipy.stats.scoreatpercentile(nstars.values(),
options.stars_per)
print 'done.'
print "%sNumber of stars at percentile = %d, taking the images with at " \
"least this number of sources..." % (style.prefix, min_nstars) ,
sys.stdout.flush()
most_populated_images = [
path for path, stars in nstars.iteritems() if stars >= min_nstars
]
logging.debug("There are %s images with a number of stars at the %.2f " \
"percentile" % (len(most_populated_images), options.stars_per))
logging.debug("Identifying the image with the lowest FWHM")
print 'done.'
print "%sFinally, finding the image with the lowest FWHM among these " \
"%d images..." % (style.prefix, len(most_populated_images)),
sys.stdout.flush()
# Find the image with the best seeing (lowest FWHM)
best_seeing = min(most_populated_images, key=lambda path: fwhms[path])
logging.debug("Best-seeing image: %s" % path)
logging.debug("Best-seeing image FWHM = %.3f" % fwhms[best_seeing])
logging.debug("Best-seeing image elongation = %.3f" % elongs[best_seeing])
logging.debug("Best-seeing image sources = %d" % nstars[best_seeing])
assert best_seeing not in fwhm_discarded
assert best_seeing not in elong_discarded
print 'done.'
print "%sBest-seeing image = %s, with %d sources and a FWHM of %.3f pixels" % \
(style.prefix, best_seeing, nstars[best_seeing], fwhms[best_seeing])
# The subdirectories are created only if at least one image is going to be
# discarded. We do not want empty directories in case no image is discarded
# because of its full-width at half maximum (FWHM) or elongation.
if fwhm_discarded:
methods.determine_output_dir(fwhm_dir, quiet=True)
if elong_discarded:
methods.determine_output_dir(elong_dir, quiet=True)
# Finally, copy all the FITS images to the output directory
processed = 0
for path in sorted(all_images):
# Add the suffix to the basename of the FITS image
root, ext = os.path.splitext(os.path.basename(path))
output_filename = root + options.suffix + ext
logging.debug("Basename '%s' + '%s' becomes '%s'" % \
(path, options.suffix, output_filename))
if path in fwhm_discarded:
output_path = os.path.join(fwhm_dir, output_filename)
logging.debug("%s was discarded because of its FWHM" % path)
logging.debug("%s to be copied to subdirectory %s" %
(path, fwhm_dir))
history_msg1 = "Image discarded by LEMON on %s" % methods.utctime()
history_msg2 = "[Discarded] FWHM = %.3f pixels, maximum allowed value = %.3f" % \
(fwhms[path], maximum_fwhm)
elif path in elong_discarded:
output_path = os.path.join(elong_dir, output_filename)
logging.debug("%s was discarded because of its elongation ratio" %
path)
logging.debug("%s to be copied to subdirectory %s" %
(path, elong_dir))
history_msg1 = "Image discarded by LEMON on %s" % methods.utctime()
history_msg2 = "[Discarded] Elongation = %.3f, maximum allowed value = %.3f" % \
(elongs[path], maximum_elong)
elif path == best_seeing:
# Retain original name if --filename is an empty string
if not options.bseeingfn:
filename = output_filename
else:
filename = options.bseeingfn
output_path = os.path.join(output_dir, filename)
logging.debug("%s is the best-seeing image" % path)
logging.debug("%s to be copied to directory %s with name %s" % \
(path, output_dir, options.bseeingfn))
history_msg1 = "Image identified by LEMON as the 'best-seeing' one"
history_msg2 = "FWHM = %.3f | Elongation = %.3f | Sources: %d (at %.2f percentile)" % \
(fwhms[path], elongs[path], nstars[path], options.stars_per)
else:
output_path = os.path.join(output_dir, output_filename)
logging.debug("%s to be copied to %s" % (path, output_dir))
history_msg1 = "Image FWHM = %.3f" % fwhms[path]
history_msg2 = "Image elongation = %.3f" % elongs[path]
if os.path.exists(output_path) and not options.overwrite:
msg = ("%sError. Output FITS file '%s' already exists. "
"You need to use --overwrite.")
args = style.prefix, output_path
print msg % args
print style.error_exit_message
return 1
else:
src = seeing_tmp_paths[path]
shutil.move(src, output_path)
methods.owner_writable(output_path, True) # chmod u+w
logging.debug("%s copied to %s" % (path, output_path))
output_img = fitsimage.FITSImage(output_path)
output_img.add_history(history_msg1)
output_img.add_history(history_msg2)
logging.debug("%s: FITS header updated (HISTORY keywords)" % path)
# Copy the FWHM to the FITS header, for future reference
comment = "Margin = %d, SNR percentile = %.3f" % (options.margin,
options.per)
output_img.update_keyword(options.fwhmk, fwhms[path], comment=comment)
logging.debug("%s: FITS header updated (%s keyword)" %
(path, options.fwhmk))
print "%sFITS image %s saved to %s" % (style.prefix, path, output_path)
processed += 1
print "%sA total of %d images was saved to directory '%s'." % (
style.prefix, processed, output_dir)
print "%sWe're done ^_^" % style.prefix
return 0