def reprocess_all_data(obstory_id):
db = meteorpi_db.MeteorDatabase(mod_settings.settings['dbFilestore'])
db.con.execute("SELECT m.time FROM archive_metadata m "
"INNER JOIN archive_observatories l ON m.observatory = l.uid "
"AND l.publicId = %s AND m.time>0 "
"ORDER BY m.time ASC LIMIT 1",
(obstory_id,))
first_seen = 0
results = db.con.fetchall()
if results:
first_seen = results[0]['time']
db.con.execute("SELECT m.time FROM archive_metadata m "
"INNER JOIN archive_observatories l ON m.observatory = l.uid "
"AND l.publicId = %s AND m.time>0 "
"ORDER BY m.time DESC LIMIT 1",
(obstory_id,))
last_seen = 0
results = db.con.fetchall()
if results:
last_seen = results[0]['time']
day = 86400
utc = math.floor(first_seen / day) * day + day / 2
while utc < last_seen:
orientation_calc(obstory_id=obstory_id,
utc_to_study=utc,
utc_now=mod_log.get_utc(),
utc_must_stop=0)
utc += day
def get_gps_fix():
log_txt("Waiting for GPS link")
# Run gpsFix.py, which returns JSON output to stdout
cmd_ = os.path.join(mod_settings.settings['pythonPath'], "gpsFix.py")
gps_process = subprocess.Popen(cmd_, shell=True, stdout=subprocess.PIPE)
gps_fix_json = gps_process.stdout.read()
try:
gps_result = json.loads(gps_fix_json)
except ValueError:
log_txt("Could not read valid JSON response from gpsFix.py")
gps_result = False
# If true, we get a structure with fields "offset", "latitude" and "longitude"
if isinstance(gps_result, dict):
t_offset = gps_result['offset']
gps_latitude = gps_result['latitude']
gps_longitude = gps_result['longitude']
gps_altitude = gps_result['altitude']
log_txt("GPS link achieved")
log_txt(
"Longitude = %.6f ; Latitude = %.6f ; Altitude = %.6f ; Clock offset: %.2f sec behind."
% (gps_longitude, gps_latitude, gps_altitude, t_offset))
set_utc_offset(t_offset)
# Use the time shell command to update the system clock (required root access)
log_txt("Trying to update system clock")
utc_now = get_utc()
os.system("date -s @%d" % utc_now)
# Because the above may fail if we don't have root access, as a fallback we recalculate the clock offset
t_offset = utc_now - time.time()
set_utc_offset(t_offset)
log_txt(
"Revised clock offset after trying to set the system clock: %.2f sec behind."
% t_offset)
return {
'latitude': gps_latitude,
'longitude': gps_longitude,
'altitude': gps_altitude
}
# If false, we didn't manage to establish a GPS link
else:
log_txt("Gave up waiting for a GPS link")
return None
def export_data(db, utc_now, utc_must_stop=0):
log_txt("Starting export of images and events")
# Work out how long we can do exporting for
utc_stop = get_utc() + (utc_must_stop - utc_now)
# Search for items which need exporting
for export_config in db.get_export_configurations():
if export_config.enabled:
db.mark_entities_to_export(export_config)
db.commit()
# Create an exporter instance
exporter = MeteorExporter(db=db)
# Loop until either we run out of time, or we run out of files to export
max_failures = 4
fail_count = 0
while ((not utc_must_stop) or
(time.time() < utc_stop)) and (fail_count < max_failures):
state = exporter.handle_next_export()
db.commit()
if not state:
log_txt("Finished export of images and events")
break
print "Export status: %s" % state.state
if state.state == "failed":
log_txt("Backing off, because an export failed")
time.sleep([30, 300, 600, 1200, 2400][fail_count])
fail_count += 1
else:
fail_count = 0
# Exit
if fail_count >= max_failures:
log_txt("Exceeded maximum allowed number of failures: giving up.")
% t_offset)
return {
'latitude': gps_latitude,
'longitude': gps_longitude,
'altitude': gps_altitude
}
# If false, we didn't manage to establish a GPS link
else:
log_txt("Gave up waiting for a GPS link")
return None
# Fetch observatory status, e.g. location, etc
time_now = get_utc()
log_txt("Fetching observatory status")
latitude = installation_info.local_conf['latitude']
longitude = installation_info.local_conf['longitude']
altitude = 0
latest_position_update = 0
flag_gps = 0
obstory_status = None
# If this observatory doesn't exist in the database, create it now with information from installation_info
if not db.has_obstory_id(obstory_id):
log_txt("Observatory '%s' is not set up. Using default settings." %
obstory_id)
db.register_obstory(
obstory_id=installation_info.local_conf['observatoryId'],
obstory_name=installation_info.local_conf['observatoryName'],
hwm_new[obstory_id][hwm_output] = hwm_old[obstory_id][hwm_output]
# Some tasks produce output files with different timestamps to the input file. Specifically, non-live
# observing produces output over the entire length of a video. hwm_margin is the maximum size of this span
hwm_margin = ((mod_settings.settings['videoMaxRecordTime'] -
5) if hwm_output == "rawvideo" else 0.1)
job_list = []
# Loop over each of the input file search patterns that this task group is to be applied to
for task in task_list:
[in_dir, out_dirs, in_ext, out_ext, cmd] = task
# Loop recursively over all files in the input directory
for dir_name, subdir_list, file_list in os.walk(in_dir):
for f in file_list:
if quit_time and (get_utc() > quit_time):
raise TimeOut
input_file = os.path.join(dir_name, f)
# Input files must have correct extension and non-zero size to be processed
if f.endswith(".%s" % in_ext) and (
os.path.getsize(input_file) > 0):
# Extract observation time from image filename. If this fails, reject the file.
utc = mod_log.filename_to_utc(f)
if utc < 0:
continue
# Add this job to our list of things to do
job_counter += 1
mask_file = "/tmp/triggermask_%d_%d.txt" % (
cwd = os.getcwd()
os.chdir(tmp_dir)
# Run the C program camfit, which attempts to stack together all of the images used by orientationCalc using
# different barrel correction coefficients. If there are no lens distortions, the image should overlay each
# other perfectly when they are stacked together. Otherwise, they won't overlay each other, because the
# gnomonic transformations won't have properly de-rotated the sky. Iteratively try different barrel corrections
# until we find a set which work well
os.system("%s/bin/camfit %s > camFitOutput" % (mod_settings.settings['stackerPath'], image_list_filename))
# The last line of output from camfit will contain the barrel distortion correction parameters a, b, c
# separated by spaces
camfit_output = open("camFitOutput").readlines()
camfit_last_line = camfit_output[-1]
print "Best fitting barrel distortion parameters were:\n%s\n\n" % camfit_last_line
# Change back into the working directory
os.chdir(cwd)
# If we're called as a script, run the method orientationCalc()
if __name__ == "__main__":
_obstory_name = installation_info.local_conf['observatoryName']
_utc_now = mod_log.get_utc()
if len(sys.argv) > 1:
_obstory_name = sys.argv[1]
if len(sys.argv) > 2:
_utc_now = float(sys.argv[2])
lens_fit(_obstory_name, _utc_now)
def orientation_calc(obstory_id, utc_to_study, utc_now, utc_must_stop=0):
log_prefix = "[%12s %s]" % (obstory_id, mod_astro.time_print(utc_to_study))
log_txt("%s Starting calculation of camera alignment" % log_prefix)
# Mathematical constants
deg = math.pi / 180
rad = 180 / math.pi
# This is an estimate of the *maximum* angular width we expect images to have.
# It should be within a factor of two of correct!
estimated_image_scale = installation_info.local_conf['estimated_image_scale']
# When passing images to astrometry.net, only work on the central portion, as this will have least bad distortion
fraction_x = 0.4
fraction_y = 0.4
# Path the binary barrel-correction tool
barrel_correct = os.path.join(mod_settings.settings['stackerPath'], "barrel")
# Calculate time span to use images from
utc_min = utc_to_study
utc_max = utc_to_study + 3600 * 24
db = meteorpi_db.MeteorDatabase(mod_settings.settings['dbFilestore'])
# Fetch observatory status
obstory_info = db.get_obstory_from_id(obstory_id)
obstory_status = None
if obstory_info and ('name' in obstory_info):
obstory_status = db.get_obstory_status(obstory_name=obstory_info['name'], time=utc_now)
if not obstory_status:
log_txt("%s Aborting -- no observatory status available." % log_prefix)
db.close_db()
return
obstory_name = obstory_info['name']
# Search for background-subtracted time lapse photography within this range
search = mp.FileRecordSearch(obstory_ids=[obstory_id], semantic_type="meteorpi:timelapse/frame/bgrdSub",
time_min=utc_min, time_max=utc_max, limit=1000000)
files = db.search_files(search)
files = files['files']
# Filter out files where the sky clarity is good and the Sun is well below horizon
acceptable_files = []
for f in files:
if db.get_file_metadata(f.id, 'meteorpi:skyClarity') < 27:
continue
if db.get_file_metadata(f.id, 'meteorpi:sunAlt') > -4:
continue
acceptable_files.append(f)
log_msg = ("%s %d still images in search period. %d meet sky quality requirements." %
(log_prefix, len(files), len(acceptable_files)))
# If we don't have enough images, we can't proceed to get a secure orientation fit
if len(acceptable_files) < 6:
log_txt("%s Not enough suitable images." % log_msg)
db.close_db()
return
log_txt(log_msg)
# We can't afford to run astrometry.net on too many images, so pick the 20 best ones
acceptable_files.sort(key=lambda f: db.get_file_metadata(f.id, 'meteorpi:skyClarity'))
acceptable_files.reverse()
acceptable_files = acceptable_files[0:20]
# Make a temporary directory to store files in.
# This is necessary as astrometry.net spams the cwd with lots of temporary junk
cwd = os.getcwd()
pid = os.getpid()
tmp = "/tmp/dcf21_orientationCalc_%d" % pid
# log_txt("Created temporary directory <%s>" % tmp)
os.system("mkdir %s" % tmp)
os.chdir(tmp)
# Loop over selected images and use astrometry.net to find their orientation
fits = []
fit_list = []
alt_az_list = []
count = 0
for f in acceptable_files:
img_name = f.file_name
fit_obj = {'f': f, 'i': count, 'fit': False}
fits.append(fit_obj)
filename = db.file_path_for_id(f.id)
if not os.path.exists(filename):
log_txt("%s Error! File <%s> is missing!" % (log_prefix, filename))
continue
# 1. Copy image into working directory
os.system("cp %s %s_tmp.png" % (filename, img_name))
# 2. Barrel-correct image
os.system("%s %s_tmp.png %.6f %.6f %.6f %s_tmp2.png" % (barrel_correct, img_name,
obstory_status['lens_barrel_a'],
obstory_status['lens_barrel_b'],
obstory_status['lens_barrel_c'],
img_name))
# 3. Pass only central portion of image to astrometry.net. It's not very reliable with wide-field images
d = image_dimensions("%s_tmp2.png" % img_name)
os.system(
"convert %s_tmp2.png -colorspace sRGB -define png:format=png24 -crop %dx%d+%d+%d +repage %s_tmp3.png"
% (img_name,
fraction_x * d[0], fraction_y * d[1],
(1 - fraction_x) * d[0] / 2, (1 - fraction_y) * d[1] / 2,
img_name))
fit_obj['fname_processed'] = '%s_tmp3.png' % img_name
fit_obj['fname_original'] = '%s_tmp.png' % img_name
fit_obj['dims'] = d # Dimensions of *original* image
count += 1
# Now pass processed image to astrometry.net for alignment
for fit in fits:
f = fit['f']
# Check that we've not run out of time
if utc_must_stop and (mod_log.get_utc() > utc_must_stop):
log_txt("%s We have run out of time! Aborting." % log_prefix)
continue
log_msg = ("Processed image <%s> from time <%s> -- skyClarity=%.1f. " %
(f.id, mod_astro.time_print(f.file_time),
db.get_file_metadata(f.id, 'meteorpi:skyClarity')))
# How long should we allow astrometry.net to run for?
if mod_settings.settings['i_am_a_rpi']:
timeout = "6m"
else:
timeout = "50s"
# Run astrometry.net. Insert --no-plots on the command line to speed things up.
astrometry_start_time = mod_log.get_utc()
estimated_width = 2 * math.atan(math.tan(estimated_image_scale / 2 * deg) * fraction_x) * rad
os.system("timeout %s /usr/local/astrometry/bin/solve-field --no-plots --crpix-center --scale-low %.1f "
"--scale-high %.1f --odds-to-tune-up 1e4 --odds-to-solve 1e7 --overwrite %s > txt"
% (timeout,
estimated_width * 0.6,
estimated_width * 1.2,
fit['fname_processed']))
astrometry_time_taken = mod_log.get_utc() - astrometry_start_time
log_msg += ("Astrometry.net took %d sec. " % astrometry_time_taken)
# Parse the output from astrometry.net
fit_text = open("txt").read()
# log_txt(fit_text)
test = re.search(r"\(RA H:M:S, Dec D:M:S\) = \(([\d-]*):(\d\d):([\d.]*), [+]?([\d-]*):(\d\d):([\d\.]*)\)",
fit_text)
if not test:
log_txt("%s FAIL(POS): %s" % (log_prefix, log_msg))
continue
ra_sign = sgn(float(test.group(1)))
ra = abs(float(test.group(1))) + float(test.group(2)) / 60 + float(test.group(3)) / 3600
if ra_sign < 0:
ra *= -1
dec_sign = sgn(float(test.group(4)))
dec = abs(float(test.group(4))) + float(test.group(5)) / 60 + float(test.group(6)) / 3600
if dec_sign < 0:
dec *= -1
test = re.search(r"up is [+]?([-\d\.]*) degrees (.) of N", fit_text)
if not test:
log_txt("%s FAIL(PA ): %s" % (log_prefix, log_msg))
continue
# celestial_pa is the position angle of the upward vector in the centre of the image, counterclockwise
# from celestial north.
# * It is zero if the pole star is vertical above the centre of the image.
# * If the pole star is in the top-right of an image, expect it to be around -45 degrees.
celestial_pa = float(test.group(1))
# * This 180 degree rotation appears because when astrometry.net says "up" it means the bottom of the image!
celestial_pa += 180
if test.group(2) == "W":
celestial_pa *= -1
while celestial_pa > 180:
celestial_pa -= 360
while celestial_pa < -180:
celestial_pa += 360
test = re.search(r"Field size: ([\d\.]*) x ([\d\.]*) deg", fit_text)
if not test:
log_txt("%s FAIL(SIZ): %s" % (log_prefix, log_msg))
continue
# Expand reported size of image to whole image, not just the central tile we sent to astrometry.net
scale_x = 2 * math.atan(math.tan(float(test.group(1)) / 2 * deg) * (1 / fraction_x)) * rad
scale_y = 2 * math.atan(math.tan(float(test.group(2)) / 2 * deg) * (1 / fraction_y)) * rad
# Work out alt-az of reported (RA,Dec) using known location of camera. Fits returned in degrees.
alt_az = mod_astro.alt_az(ra, dec, fit['f'].file_time,
obstory_status['latitude'], obstory_status['longitude'])
# Get celestial coordinates of the local zenith
ra_dec_zenith = mod_astro.get_zenith_position(obstory_status['latitude'],
obstory_status['longitude'],
fit['f'].file_time)
ra_zenith = ra_dec_zenith['ra']
dec_zenith = ra_dec_zenith['dec']
# Work out the position angle of the zenith, counterclockwise from north, as measured at centre of frame
zenith_pa = mod_gnomonic.position_angle(ra, dec, ra_zenith, dec_zenith)
# Calculate the position angle of the zenith, clockwise from vertical, at the centre of the frame
# If the camera is roughly upright, this ought to be close to zero!
camera_tilt = zenith_pa - celestial_pa
while camera_tilt < -180:
camera_tilt += 360
while camera_tilt > 180:
camera_tilt -= 360
log_txt("%s PASS : %s" % (log_prefix, log_msg))
log_txt("%s FIT : RA: %7.2fh. Dec %7.2f deg. PA %6.1f deg. ScaleX %6.1f. ScaleY %6.1f. "
"Zenith at (%.2f h,%.2f deg). PA Zenith %.2f deg. "
"Alt: %7.2f deg. Az: %7.2f deg. Tilt: %7.2f deg." %
(log_prefix, ra, dec, celestial_pa, scale_x, scale_y, ra_zenith, dec_zenith, zenith_pa,
alt_az[0], alt_az[1], camera_tilt))
# Store information about fit
fit.update({'fit': True, 'ra': ra, 'dec': dec, 'pa': celestial_pa, 'sx': scale_x, 'sy': scale_y,
'camera_tilt': camera_tilt})
fit_list.append(fit)
alt_az_list.append(alt_az)
# Average the resulting fits
if len(fit_list) < 4:
log_txt("%s ABORT : astrometry.net only managed to fit %2d images." % (log_prefix, len(fit_list)))
db.close_db()
os.chdir(cwd)
os.system("rm -Rf %s" % tmp)
return
pa_list = [i['camera_tilt'] * deg for i in fits if i['fit']]
pa_best = mod_astro.mean_angle(pa_list)[0]
scale_x_list = [i['sx'] * deg for i in fits if i['fit']]
scale_x_best = mod_astro.mean_angle(scale_x_list)[0]
scale_y_list = [i['sy'] * deg for i in fits if i['fit']]
scale_y_best = mod_astro.mean_angle(scale_y_list)[0]
# Convert alt-az fits into radians
alt_az_list_r = [[i * deg for i in j] for j in alt_az_list]
[alt_az_best, alt_az_error] = mod_astro.mean_angle_2d(alt_az_list_r)
# Print fit information
success = (alt_az_error * rad < 0.6)
if success:
adjective = "SUCCESSFUL"
else:
adjective = "REJECTED"
log_txt("%s %s ORIENTATION FIT (from %2d images). "
"Alt: %.2f deg. Az: %.2f deg. PA: %.2f deg. ScaleX: %.2f deg. ScaleY: %.2f deg. "
"Uncertainty: %.2f deg." % (log_prefix, adjective, len(fit_list),
alt_az_best[0] * rad,
alt_az_best[1] * rad,
pa_best * rad,
scale_x_best * rad,
scale_y_best * rad,
alt_az_error * rad))
# Update observatory status
if success:
user = mod_settings.settings['meteorpiUser']
utc = utc_to_study
db.register_obstory_metadata(obstory_name, "orientation_altitude", alt_az_best[0] * rad, utc, user)
db.register_obstory_metadata(obstory_name, "orientation_azimuth", alt_az_best[1] * rad, utc, user)
db.register_obstory_metadata(obstory_name, "orientation_error", alt_az_error * rad, utc, user)
db.register_obstory_metadata(obstory_name, "orientation_pa", pa_best * rad, utc, user)
db.register_obstory_metadata(obstory_name, "orientation_width_x_field", scale_x_best * rad, utc, user)
db.register_obstory_metadata(obstory_name, "orientation_width_y_field", scale_y_best * rad, utc, user)
db.commit()
db.close_db()
# Clean up and exit
os.chdir(cwd)
os.system("rm -Rf %s" % tmp)
return
def database_import(db):
# Change into the directory where data files are kept
cwd = os.getcwd()
os.chdir(mod_settings.settings['dataPath'])
# Lists of high water marks, showing where we've previously got up to in importing observations
hwm_old = {} # hwm_old[obstory_id] = old "import" high water mark
hwm_new = {} # hwm_new[obstory_id] = new "import" high water mark
# A list of the trigger observation IDs we've created
trigger_obs_list = {} # trigger_obs_list[obstory_id][utc] = observation_id
# A list of the still image observation IDs we've created
still_img_obs_list = {}
# Loop over all of the video files and images we've created locally. For each one, we create a new observation
# object if there are no other files from the same observatory with the same time stamp.
# We ignore trigger images if there's no video file with the same time stamp.
for [
glob_pattern, observation_list, mime_type, obs_type,
create_new_observations
] in [[
"triggers_vid_processed/*/*.mp4", trigger_obs_list, "video/mp4",
"movingObject", True
],
[
"timelapse_img_processed/*/*.png", still_img_obs_list,
"image/png", "timelapse", True
],
[
"triggers_img_processed/*/*.png", trigger_obs_list, "image/png",
"", False
]]:
# Create a list of all the files which match this particular wildcard
file_list = glob.glob(glob_pattern)
file_list.sort()
log_txt(
"Registering files which match the wildcard <%s> -- %d files." %
(glob_pattern, len(file_list)))
# Loop over all the files
for file_name in file_list:
file_stub = file_name[:-4]
utc = mod_log.filename_to_utc(file_name) + 0.01
# Local images and video all have meta data in a file with a .txt file extension
meta_file = "%s.txt" % file_stub # File containing metadata
meta_dict = mod_daytimejobs.file_to_dict(
meta_file) # Dictionary of image metadata
assert "obstoryId" in meta_dict, "File <%s> does not have a obstoryId set." % file_name
# Get the ID and name of the observatory that is responsible for this file
obstory_id = meta_dict["obstoryId"]
obstory_name = get_obstory_name_from_id(db=db,
obstory_id=obstory_id)
if obstory_id not in hwm_old:
hwm_old[obstory_id] = db.get_high_water_mark(
mark_type="import", obstory_name=obstory_name)
if hwm_old[obstory_id] is None:
hwm_old[obstory_id] = 0
hwm_new[obstory_id] = hwm_old[obstory_id]
# If this file is older than the pre-existing high water mark for files we've imported, ignore it
# We've probably already imported it before
if utc < hwm_old[obstory_id]:
continue
print "Registering file <%s>, with obstoryId <%s>." % (file_name,
obstory_id)
# See if we already have an observation with this time stamp. If not, create one
created_new_observation = False
if not ((obstory_id in observation_list) and
(utc in observation_list[obstory_id])):
if not create_new_observations:
continue
obs_obj = db.register_observation(obstory_name=obstory_name,
obs_time=utc,
obs_type=obs_type,
user_id=user,
obs_meta=[])
obs_id = obs_obj.id
dict_tree_append(observation_list, [obstory_id, utc], obs_id)
created_new_observation = True
print "Created new observation with ID <%s>." % obs_id
else:
obs_id = observation_list[obstory_id][utc]
# Compile a list of metadata objects to associate with this file
metadata_objs = metadata_to_object_list(db, utc, obs_id, meta_dict)
# If we've newly created an observation object for this file, we transfer the file's metadata
# to the observation as well
if created_new_observation:
for metadata_obj in metadata_objs:
db.set_observation_metadata(user, obs_id, metadata_obj)
# Import the file itself into the database
semantic_type = local_filename_to_semantic_type(file_name)
db.register_file(file_path=file_name,
user_id=user,
mime_type=mime_type,
semantic_type=semantic_type,
file_time=utc,
file_meta=metadata_objs,
observation_id=obs_id)
# Update this observatory's "import" high water mark to the time of the file just imported
hwm_new[obstory_id] = max(hwm_new[obstory_id], utc)
os.chdir(cwd)
# Now do some housekeeping tasks on the local database
# Create a status log file for this observatory (so the health of this system can be checked remotely)
# Use a file in /tmp to record the latest time we created a log file. It contains a unix time.
last_update_filename = "/tmp/obstoryStatus_last"
last_update_time = 0
try:
last_update_time = float(open(last_update_filename, "r").read())
except IOError:
pass
except OSError:
pass
except ValueError:
pass
# Only create a new log file if we haven't created one within the past 12 hours
if mod_log.get_utc() - last_update_time > 12 * 3600:
# Give the log file a human-readable filename
log_file_name = "/tmp/obstoryStatus_" + time.strftime(
"%Y%m%d", time.gmtime(get_utc())) + ".log"
os.system("./observatoryStatusLog.sh > %s" % log_file_name)
# Create an observation object to associate with this log file
logfile_obs = db.register_observation(
obstory_name=installation_info.local_conf['observatoryName'],
user_id=user,
obs_time=mod_log.get_utc(),
obs_type="logging",
obs_meta=[])
# Register the log file in the database and associate it with the observation above
db.register_file(file_path=log_file_name,
user_id=user,
mime_type="text/plain",
semantic_type="logfile",
file_time=get_utc(),
file_meta=[],
observation_id=logfile_obs.id)
# Update the local record of when we last created a log file observation
open(last_update_filename, "w").write("%s" % mod_log.get_utc())
# Remove old data from the local database, if it is older than the local data lifetime
db.clear_database(
obstory_names=[installation_info.local_conf['observatoryName']],
tmin=0,
tmax=get_utc() -
24 * 2400 * installation_info.local_conf['dataLocalLifetime'])
# Update the "import" high water marks for each obstory_name
for obstory_id in hwm_new.keys():
obstory_name = get_obstory_name_from_id(db=db, obstory_id=obstory_id)
db.set_high_water_mark(obstory_name=obstory_name,
mark_type="import",
time=hwm_new[obstory_id])
# Commit our changes to the database
db.commit()
os.chdir(cwd)
return