def list_triangulations(utc_min=None, utc_max=None):
"""
Display a list of all the trajectories of moving objects registered in the database.
:param utc_min:
Only show observations made after the specified time stamp.
:type utc_min:
float
:param utc_max:
Only show observations made before the specified time stamp.
:type utc_max:
float
:return:
None
"""
# Open connection to database
[db0, conn] = connect_db.connect_db()
# Compile search criteria for observation groups
where = ["g.semanticType = (SELECT uid FROM archive_semanticTypes WHERE name=\"{}\")".
format(simultaneous_event_type)
]
args = []
if utc_min is not None:
where.append("g.time>=%s")
args.append(utc_min)
if utc_max is not None:
where.append("g.time<=%s")
args.append(utc_max)
# Search for observation groups containing groups of simultaneous detections
conn.execute("""
SELECT g.publicId AS groupId, g.time AS time, am.stringValue AS objectType,
am2.floatValue AS speed, am3.floatValue AS mean_altitude, am4.floatValue AS max_angular_offset,
am5.floatValue AS max_baseline, am6.stringValue AS radiant_direction, am7.floatValue AS sight_line_count,
am8.stringValue AS path
FROM archive_obs_groups g
INNER JOIN archive_metadata am ON g.uid = am.groupId AND
am.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="web:category")
INNER JOIN archive_metadata am2 ON g.uid = am2.groupId AND
am2.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="triangulation:speed")
INNER JOIN archive_metadata am3 ON g.uid = am3.groupId AND
am3.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="triangulation:mean_altitude")
INNER JOIN archive_metadata am4 ON g.uid = am4.groupId AND
am4.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="triangulation:max_angular_offset")
INNER JOIN archive_metadata am5 ON g.uid = am5.groupId AND
am5.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="triangulation:max_baseline")
INNER JOIN archive_metadata am6 ON g.uid = am6.groupId AND
am6.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="triangulation:radiant_direction")
INNER JOIN archive_metadata am7 ON g.uid = am7.groupId AND
am7.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="triangulation:sight_line_count")
INNER JOIN archive_metadata am8 ON g.uid = am8.groupId AND
am8.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="triangulation:path")
WHERE """ + " AND ".join(where) + """
ORDER BY g.time;
""", args)
results = conn.fetchall()
# Count how many simultaneous detections we find by type
detections_by_type = {}
# Compile tally by type
for item in results:
# Add this triangulation to tally
if item['objectType'] not in detections_by_type:
detections_by_type[item['objectType']] = 0
detections_by_type[item['objectType']] += 1
# List information about each observation in turn
print("{:16s} {:20s} {:20s} {:8s} {:10s}".format("GroupId", "Time", "Object type", "Speed", "Altitude"))
for item in results:
# Print triangulation information
print("{:16s} {:20s} {:20s} {:8.0f} {:10.0f}".format(item['groupId'],
date_string(item['time']),
item['objectType'],
item['speed'],
item['mean_altitude']
))
# Report tally of events
print("\nTally of events by type:")
for event_type in sorted(detections_by_type.keys()):
print(" * {:26s}: {:6d}".format(event_type, detections_by_type[event_type]))
def do_triangulation(utc_min, utc_max, utc_must_stop):
# We need to share the list of sight lines to each moving object with the objective function that we minimise
global sight_line_list, time_span, seed_position
# Start triangulation process
logging.info(
"Triangulating simultaneous object detections between <{}> and <{}>.".
format(date_string(utc_min), date_string(utc_max)))
db = obsarchive_db.ObservationDatabase(
file_store_path=settings['dbFilestore'],
db_host=installation_info['mysqlHost'],
db_user=installation_info['mysqlUser'],
db_password=installation_info['mysqlPassword'],
db_name=installation_info['mysqlDatabase'],
obstory_id=installation_info['observatoryId'])
# Count how many objects we manage to successfully fit
outcomes = {
'successful_fits': 0,
'failed_fits': 0,
'inadequate_baseline': 0,
'error_records': 0,
'rescued_records': 0,
'insufficient_information': 0
}
# Compile search criteria for observation groups
where = [
"g.semanticType = (SELECT uid FROM archive_semanticTypes WHERE name=\"{}\")"
.format(simultaneous_event_type)
]
args = []
if utc_min is not None:
where.append("o.obsTime>=%s")
args.append(utc_min)
if utc_max is not None:
where.append("o.obsTime<=%s")
args.append(utc_max)
# Open direct connection to database
conn = db.con
# Search for observation groups containing groups of simultaneous detections
conn.execute(
"""
SELECT g.publicId AS groupId, o.publicId AS observationId, o.obsTime, f.repositoryFname,
am.stringValue AS objectType, l.publicId AS observatory
FROM archive_obs_groups g
INNER JOIN archive_obs_group_members m on g.uid = m.groupId
INNER JOIN archive_observations o ON m.childObservation = o.uid
INNER JOIN archive_observatories l ON o.observatory = l.uid
LEFT OUTER JOIN archive_files f on o.uid = f.observationId AND
f.semanticType=(SELECT uid FROM archive_semanticTypes WHERE name="pigazing:movingObject/video")
INNER JOIN archive_metadata am ON g.uid = am.groupId AND
am.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="web:category")
WHERE """ + " AND ".join(where) + """
ORDER BY o.obsTime;
""", args)
results = conn.fetchall()
# Compile list of events into list of groups
obs_groups = {}
obs_group_ids = []
for item in results:
key = item['groupId']
if key not in obs_groups:
obs_groups[key] = []
obs_group_ids.append({
'groupId': key,
'time': item['obsTime'],
'type': item['objectType']
})
obs_groups[key].append(item)
# Loop over list of simultaneous event detections
for group_info in obs_group_ids:
# Make ID string to prefix to all logging messages about this event
logging_prefix = "{date} [{obs}/{type:16s}]".format(
date=date_string(utc=group_info['time']),
obs=group_info['groupId'],
type=group_info['type'])
# If we've run out of time, stop now
time_now = time.time()
if utc_must_stop is not None and time_now > utc_must_stop:
break
# Make a list of all our sight-lines to this object, from all observatories
sight_line_list = []
observatory_list = {}
# Fetch information about each observation in turn
for item in obs_groups[group_info['groupId']]:
# Fetch metadata about this object, some of which might be on the file, and some on the observation
obs_obj = db.get_observation(observation_id=item['observationId'])
obs_metadata = {item.key: item.value for item in obs_obj.meta}
if item['repositoryFname']:
file_obj = db.get_file(
repository_fname=item['repositoryFname'])
file_metadata = {
item.key: item.value
for item in file_obj.meta
}
else:
file_metadata = {}
all_metadata = {**obs_metadata, **file_metadata}
# Project path from (x,y) coordinates into (RA, Dec)
projector = PathProjection(db=db,
obstory_id=item['observatory'],
time=item['obsTime'],
logging_prefix=logging_prefix)
path_x_y, path_ra_dec_at_epoch, path_alt_az, sight_line_list_this = projector.ra_dec_from_x_y(
path_json=all_metadata['pigazing:path'],
path_bezier_json=all_metadata['pigazing:pathBezier'],
detections=all_metadata['pigazing:detectionCount'],
duration=all_metadata['pigazing:duration'])
# Check for error
if projector.error is not None:
if projector.error in outcomes:
outcomes[projector.error] += 1
continue
# Check for notifications
for notification in projector.notifications:
if notification in outcomes:
outcomes[notification] += 1
# Add to observatory_list, now that we've checked this observatory has all necessary information
if item['observatory'] not in observatory_list:
observatory_list[item['observatory']] = projector.obstory_info
# Add sight lines from this observatory to list which combines all observatories
sight_line_list.extend(sight_line_list_this)
# If we have fewer than four sight lines, don't bother trying to triangulate
if len(sight_line_list) < 4:
logging.info(
"{prefix} -- Giving up triangulation as we only have {x:d} sight lines to object."
.format(prefix=logging_prefix, x=len(sight_line_list)))
continue
# Initialise maximum baseline between the stations which saw this objects
maximum_baseline = 0
# Check the distances between all pairs of observatories
obstory_info_list = [
Point.from_lat_lng(lat=obstory['latitude'],
lng=obstory['longitude'],
alt=0,
utc=None)
for obstory in observatory_list.values()
]
pairs = [[obstory_info_list[i], obstory_info_list[j]]
for i in range(len(obstory_info_list))
for j in range(i + 1, len(obstory_info_list))]
# Work out maximum baseline between the stations which saw this objects
for pair in pairs:
maximum_baseline = max(
maximum_baseline,
abs(pair[0].displacement_vector_from(pair[1])))
# If we have no baselines of over 1 km, don't bother trying to triangulate
if maximum_baseline < 1000:
logging.info(
"{prefix} -- Giving up triangulation as longest baseline is only {x:.0f} m."
.format(prefix=logging_prefix, x=maximum_baseline))
outcomes['inadequate_baseline'] += 1
continue
# Set time range of sight lines
time_span = [
min(item['utc'] for item in sight_line_list),
max(item['utc'] for item in sight_line_list)
]
# Create a seed point to start search for object path. We pick a point above the centroid of the observatories
# that saw the object
centroid_v = sum(item['obs_position'].to_vector()
for item in sight_line_list) / len(sight_line_list)
centroid_p = Point(x=centroid_v.x, y=centroid_v.y, z=centroid_v.z)
centroid_lat_lng = centroid_p.to_lat_lng(utc=None)
seed_position = Point.from_lat_lng(lat=centroid_lat_lng['lat'],
lng=centroid_lat_lng['lng'],
alt=centroid_lat_lng['alt'] * 2e4,
utc=None)
# Attempt to fit a linear trajectory through all of the sight lines that we have collected
parameters_initial = [0, 0, 0, 0, 0, 0]
# Solve the system of equations
# See <http://www.scipy-lectures.org/advanced/mathematical_optimization/>
# for more information about how this works
parameters_optimised = scipy.optimize.minimize(
angular_mismatch_objective,
numpy.asarray(parameters_initial),
options={
'disp': False,
'maxiter': 1e8
}).x
# Construct best-fit linear trajectory for best-fitting parameters
best_triangulation = line_from_parameters(parameters_optimised)
# logging.info("Best fit path of object is <{}>.".format(best_triangulation))
# logging.info("Mismatch of observed sight lines from trajectory are {} deg.".format(
# ["{:.1f}".format(best_triangulation.find_closest_approach(s['line'])['angular_distance'])
# for s in sight_line_list]
# ))
# Find sight line with the worst match
mismatch_list = sight_line_mismatch_list(trajectory=best_triangulation)
maximum_mismatch = max(mismatch_list)
# Reject trajectory if it deviates by more than 8 degrees from any observation
if maximum_mismatch > 8:
logging.info(
"{prefix} -- Trajectory mismatch is too great ({x:.1f} deg).".
format(prefix=logging_prefix, x=maximum_mismatch))
outcomes['failed_fits'] += 1
continue
# Convert start and end points of path into (lat, lng, alt)
start_point = best_triangulation.point(0).to_lat_lng(utc=None)
start_point['utc'] = time_span[0]
end_point = best_triangulation.point(1).to_lat_lng(utc=None)
end_point['utc'] = time_span[1]
# Calculate linear speed of object
speed = abs(best_triangulation.direction) / (
time_span[1] - time_span[0]) # m/s
# Calculate radiant direction for this object
radiant_direction_vector = best_triangulation.direction * -1
radiant_direction_coordinates = radiant_direction_vector.to_ra_dec(
) # hours, degrees
radiant_greenwich_hour_angle = radiant_direction_coordinates['ra']
radiant_dec = radiant_direction_coordinates['dec']
instantaneous_sidereal_time = sidereal_time(utc=(utc_min + utc_max) /
2) # hours
radiant_ra = radiant_greenwich_hour_angle + instantaneous_sidereal_time # hours
radiant_direction = [radiant_ra, radiant_dec]
# Store triangulated information in database
user = settings['pigazingUser']
timestamp = time.time()
triangulation_metadata = {
"triangulation:speed":
speed,
"triangulation:mean_altitude":
(start_point['alt'] + end_point['alt']) / 2 / 1e3, # km
"triangulation:max_angular_offset":
maximum_mismatch,
"triangulation:max_baseline":
maximum_baseline,
"triangulation:radiant_direction":
json.dumps(radiant_direction),
"triangulation:sight_line_count":
len(sight_line_list),
"triangulation:path":
json.dumps([start_point, end_point])
}
# Set metadata on the observation group
for metadata_key, metadata_value in triangulation_metadata.items():
db.set_obsgroup_metadata(user_id=user,
group_id=group_info['groupId'],
utc=timestamp,
meta=mp.Meta(key=metadata_key,
value=metadata_value))
# Set metadata on each observation individually
for item in obs_groups[group_info['groupId']]:
for metadata_key, metadata_value in triangulation_metadata.items():
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key=metadata_key, value=metadata_value))
# Commit metadata to database
db.commit()
# Report outcome
logging.info(
"{prefix} -- Success -- {path}; speed {mph:11.1f} mph; {sight_lines:6d} detections."
.format(
prefix=logging_prefix,
path="{:5.1f} {:5.1f} {:10.1f} -> {:5.1f} {:5.1f} {:10.1f}".
format(
start_point['lat'],
start_point['lng'],
start_point['alt'] / 1e3, # deg deg km
end_point['lat'],
end_point['lng'],
end_point['alt'] / 1e3),
mph=speed / 0.44704, # mph
sight_lines=len(sight_line_list)))
# Triangulation successful
outcomes['successful_fits'] += 1
# Update database
db.commit()
# Report how many fits we achieved
logging.info("{:d} objects successfully triangulated.".format(
outcomes['successful_fits']))
logging.info("{:d} objects could not be triangulated.".format(
outcomes['failed_fits']))
logging.info("{:d} objects had an inadequate baseline.".format(
outcomes['inadequate_baseline']))
logging.info("{:d} malformed database records.".format(
outcomes['error_records']))
logging.info("{:d} rescued database records.".format(
outcomes['rescued_records']))
logging.info("{:d} objects with incomplete data.".format(
outcomes['insufficient_information']))
# Commit changes
db.commit()
db.close_db()
def list_images(utc_min=None,
utc_max=None,
username=None,
obstory=None,
img_type=None,
obs_type=None,
stride=1):
"""
Display a list of all the images registered in the database.
:param utc_min:
Only show observations made after the specified time stamp.
:type utc_min:
float
:param utc_max:
Only show observations made before the specified time stamp.
:type utc_max:
float
:param username:
Optionally specify a username, to filter only images by a particular user
:type username:
str
:param obstory:
The public id of the observatory we are to show observations from
:type obstory:
str
:param img_type:
Only show images with this semantic type
:type img_type:
str
:param obs_type:
Only show observations with this semantic type
:type obs_type:
str
:param stride:
Only show every nth observation matching the search criteria
:type stride:
int
:return:
None
"""
# Open connection to database
[db0, conn] = connect_db.connect_db()
where = ["1"]
args = []
if utc_min is not None:
where.append("o.obsTime>=%s")
args.append(utc_min)
if utc_max is not None:
where.append("o.obsTime<=%s")
args.append(utc_max)
if username is not None:
where.append("o.userId=%s")
args.append(username)
if obstory is not None:
where.append("l.publicId=%s")
args.append(obstory)
if obs_type is not None:
where.append("ast.name=%s")
args.append(obs_type)
conn.execute(
"""
SELECT o.uid, o.userId, l.name AS place, o.obsTime
FROM archive_observations o
INNER JOIN archive_observatories l ON o.observatory = l.uid
INNER JOIN archive_semanticTypes ast ON o.obsType = ast.uid
WHERE """ + " AND ".join(where) + """
ORDER BY obsTime DESC LIMIT 200;
""", args)
results = conn.fetchall()
# List information about each observation in turn
sys.stdout.write("{:6s} {:10s} {:32s} {:17s} {:20s}\n".format(
"obsId", "Username", "Observatory", "Time", "Images"))
for counter, obs in enumerate(results):
# Only show every nth hit
if counter % stride != 0:
continue
# Print observation information
sys.stdout.write("{:6d} {:10s} {:32s} {:17s} ".format(
obs['uid'], obs['userId'], obs['place'],
date_string(obs['obsTime'])))
where = ["f.observationId=%s"]
args = [obs['uid']]
if img_type is not None:
where.append("ast.name=%s")
args.append(img_type)
# Fetch list of files in this observation
conn.execute(
"""
SELECT ast.name AS semanticType, repositoryFname, am.floatValue AS skyClarity
FROM archive_files f
INNER JOIN archive_semanticTypes ast ON f.semanticType = ast.uid
LEFT OUTER JOIN archive_metadata am ON f.uid = am.fileId AND
am.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="pigazing:skyClarity")
WHERE """ + " AND ".join(where) + """;
""", args)
files = conn.fetchall()
for count, item in enumerate(files):
if count > 0:
sys.stdout.write("\n{:69s}".format(""))
if item['skyClarity'] is None:
item['skyClarity'] = 0
sys.stdout.write("{:40s} {:32s} {:10.1f}".format(
item['semanticType'], item['repositoryFname'],
item['skyClarity']))
sys.stdout.write("\n")
def list_orientation_fixes(obstory_id, utc_min, utc_max):
"""
List all the orientation fixes for a particular observatory.
:param obstory_id:
The ID of the observatory we want to list orientation fixes for.
:param utc_min:
The start of the time period in which we should list orientation fixes (unix time).
:param utc_max:
The end of the time period in which we should list orientation fixes (unix time).
:return:
None
"""
# Open connection to database
[db0, conn] = connect_db.connect_db()
# Start compiling list of orientation fixes
orientation_fixes = []
# Select observations with orientation fits
conn.execute(
"""
SELECT am1.floatValue AS altitude, am2.floatValue AS azimuth, am3.floatValue AS tilt,
am4.floatValue AS width_x_field, am5.floatValue AS width_y_field,
o.obsTime AS time
FROM archive_observations o
INNER JOIN archive_metadata am1 ON o.uid = am1.observationId AND
am1.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:altitude")
INNER JOIN archive_metadata am2 ON o.uid = am2.observationId AND
am2.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:azimuth")
INNER JOIN archive_metadata am3 ON o.uid = am3.observationId AND
am3.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:tilt")
INNER JOIN archive_metadata am4 ON o.uid = am4.observationId AND
am4.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:width_x_field")
INNER JOIN archive_metadata am5 ON o.uid = am5.observationId AND
am5.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:width_y_field")
WHERE
o.observatory = (SELECT uid FROM archive_observatories WHERE publicId=%s) AND
o.obsTime BETWEEN %s AND %s;
""", (obstory_id, utc_min, utc_max))
results = conn.fetchall()
for item in results:
orientation_fixes.append({
'time': item['time'],
'average': False,
'fit': item
})
# Select observatory orientation fits
conn.execute(
"""
SELECT am1.floatValue AS altitude, am2.floatValue AS azimuth, am3.floatValue AS tilt,
am4.floatValue AS width_x_field, am5.floatValue AS width_y_field,
am1.time AS time
FROM archive_observatories o
INNER JOIN archive_metadata am1 ON o.uid = am1.observatory AND
am1.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:altitude")
INNER JOIN archive_metadata am2 ON o.uid = am2.observatory AND am2.time=am1.time AND
am2.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:azimuth")
INNER JOIN archive_metadata am3 ON o.uid = am3.observatory AND am3.time=am1.time AND
am3.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:tilt")
INNER JOIN archive_metadata am4 ON o.uid = am4.observatory AND am4.time=am1.time AND
am4.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:width_x_field")
INNER JOIN archive_metadata am5 ON o.uid = am5.observatory AND am5.time=am1.time AND
am5.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:width_y_field")
WHERE
o.publicId=%s AND
am1.time BETWEEN %s AND %s;
""", (obstory_id, utc_min, utc_max))
results = conn.fetchall()
for item in results:
orientation_fixes.append({
'time': item['time'],
'average': True,
'fit': item
})
# Sort fixes by time
orientation_fixes.sort(key=itemgetter('time'))
# Display column headings
print("""\
{:1s} {:16s} {:9s} {:9s} {:9s} {:8s} {:8s}\
""".format("", "Time", "Alt", "Az", "Tilt", "FoV X", "FoV Y"))
# Display fixes
for item in orientation_fixes:
print("""\
{:s} {:16s} {:9.4f} {:9.4f} {:9.4f} {:8.3f} {:8.3f} {:s}\
""".format("\n>" if item['average'] else " ", date_string(item['time']),
item['fit']['altitude'], item['fit']['azimuth'],
item['fit']['tilt'], item['fit']['width_x_field'],
item['fit']['width_y_field'], "\n" if item['average'] else ""))
# Clean up and exit
return
def frame_drop_detection(utc_min, utc_max):
"""
Detect video frame drop events between the unix times <utc_min> and <utc_max>.
:param utc_min:
The start of the time period in which we should search for video frame drop (unix time).
:type utc_min:
float
:param utc_max:
The end of the time period in which we should search for video frame drop (unix time).
:type utc_max:
float
:return:
None
"""
# Open connection to image archive
db = obsarchive_db.ObservationDatabase(
file_store_path=settings['dbFilestore'],
db_host=installation_info['mysqlHost'],
db_user=installation_info['mysqlUser'],
db_password=installation_info['mysqlPassword'],
db_name=installation_info['mysqlDatabase'],
obstory_id=installation_info['observatoryId'])
logging.info("Starting video frame drop detection.")
# Count how many images we manage to successfully fit
outcomes = {
'frame_drop_events': 0,
'non_frame_drop_events': 0,
'error_records': 0,
'rescued_records': 0
}
# Status update
logging.info("Searching for frame drops within period {} to {}".format(
date_string(utc_min), date_string(utc_max)))
# Open direct connection to database
conn = db.con
# Search for meteors within this time period
conn.execute(
"""
SELECT ao.obsTime, ao.publicId AS observationId, f.repositoryFname, l.publicId AS observatory, am6.stringValue AS type
FROM archive_observations ao
LEFT OUTER JOIN archive_files f ON (ao.uid = f.observationId AND
f.semanticType=(SELECT uid FROM archive_semanticTypes WHERE name="pigazing:movingObject/video"))
INNER JOIN archive_observatories l ON ao.observatory = l.uid
LEFT OUTER JOIN archive_metadata am6 ON ao.uid = am6.observationId AND
am6.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="web:category")
WHERE ao.obsType=(SELECT uid FROM archive_semanticTypes WHERE name='pigazing:movingObject/') AND
ao.obsTime BETWEEN %s AND %s
ORDER BY ao.obsTime
""", (utc_min, utc_max))
results = conn.fetchall()
# Display logging list of the videos we are going to work on
logging.info("Searching for dropped frames within {:d} videos.".format(
len(results)))
# Analyse each video in turn
for item_index, item in enumerate(results):
# Fetch metadata about this object, some of which might be on the file, and some on the observation
obs_obj = db.get_observation(observation_id=item['observationId'])
obs_metadata = {item.key: item.value for item in obs_obj.meta}
if item['repositoryFname']:
file_obj = db.get_file(repository_fname=item['repositoryFname'])
file_metadata = {item.key: item.value for item in file_obj.meta}
else:
file_metadata = {}
all_metadata = {**obs_metadata, **file_metadata}
# Check we have all required metadata
if ('pigazing:path' not in all_metadata) or ('pigazing:videoStart'
not in all_metadata):
logging.info(
"Cannot process <{}> due to inadequate metadata.".format(
item['observationId']))
continue
# Make ID string to prefix to all logging messages about this event
logging_prefix = "{date} [{obs}/{type:16s}]".format(
date=date_string(utc=item['obsTime']),
obs=item['observationId'],
type=item['type'] if item['type'] is not None else '')
# Read path of the moving object in pixel coordinates
path_json = all_metadata['pigazing:path']
try:
path_x_y = json.loads(path_json)
except json.decoder.JSONDecodeError:
# Attempt JSON repair; sometimes JSON content gets truncated
original_json = path_json
fixed_json = "],[".join(original_json.split("],[")[:-1]) + "]]"
try:
path_x_y = json.loads(fixed_json)
# logging.info("{prefix} -- RESCUE: In: {detections:.0f} / {duration:.1f} sec; "
# "Rescued: {count:d} / {json_span:.1f} sec".format(
# prefix=logging_prefix,
# detections=all_metadata['pigazing:detections'],
# duration=all_metadata['pigazing:duration'],
# count=len(path_x_y),
# json_span=path_x_y[-1][3] - path_x_y[0][3]
# ))
outcomes['rescued_records'] += 1
except json.decoder.JSONDecodeError:
logging.info(
"{prefix} -- !!! JSON error".format(prefix=logging_prefix))
outcomes['error_records'] += 1
continue
# Check number of points in path
path_len = len(path_x_y)
# Make list of object speed at each point
path_speed = [] # pixels/sec
path_distance = []
for i in range(path_len - 1):
pixel_distance = hypot(path_x_y[i + 1][0] - path_x_y[i][0],
path_x_y[i + 1][1] - path_x_y[i][1])
time_interval = (path_x_y[i + 1][3] - path_x_y[i][3]) + 1e-8
speed = pixel_distance / time_interval
path_speed.append(speed)
path_distance.append(pixel_distance)
# Start making a list of frame-drop events
frame_drop_points = []
# Scan through for points with anomalously high speed
scan_half_window = 4
for i in range(len(path_speed)):
scan_min = max(0, i - scan_half_window)
scan_max = min(scan_min + 2 * scan_half_window,
len(path_speed) - 1)
median_speed = max(np.median(path_speed[scan_min:scan_max]), 1)
if (path_distance[i] > 16) and (path_speed[i] > 4 * median_speed):
break_time = np.mean([path_x_y[i + 1][3], path_x_y[i][3]])
video_time = break_time - all_metadata['pigazing:videoStart']
break_distance = path_distance[i]
# significance = path_speed[i]/median_speed
frame_drop_points.append([
i + 1,
float("%.4f" % break_time),
float("%.1f" % video_time),
round(break_distance)
])
# Report result
if len(frame_drop_points) > 0:
logging.info("{prefix} -- {x}".format(prefix=logging_prefix,
x=frame_drop_points))
# Store frame-drop list
user = settings['pigazingUser']
timestamp = time.time()
db.set_observation_metadata(user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(
key="frame_drop:list",
value=json.dumps(frame_drop_points)))
# Video successfully analysed
if len(frame_drop_points) == 0:
outcomes['non_frame_drop_events'] += 1
else:
outcomes['frame_drop_events'] += 1
# Update database
db.commit()
# Report how many fits we achieved
logging.info("{:d} videos with frame-drop.".format(
outcomes['frame_drop_events']))
logging.info("{:d} videos without frame-drop.".format(
outcomes['non_frame_drop_events']))
logging.info("{:d} malformed database records.".format(
outcomes['error_records']))
logging.info("{:d} rescued database records.".format(
outcomes['rescued_records']))
# Clean up and exit
db.commit()
db.close_db()
return
def satellite_determination(utc_min, utc_max):
"""
Estimate the identity of spacecraft observed between the unix times <utc_min> and <utc_max>.
:param utc_min:
The start of the time period in which we should determine the identity of spacecraft (unix time).
:type utc_min:
float
:param utc_max:
The end of the time period in which we should determine the identity of spacecraft (unix time).
:type utc_max:
float
:return:
None
"""
# Open connection to image archive
db = obsarchive_db.ObservationDatabase(
file_store_path=settings['dbFilestore'],
db_host=installation_info['mysqlHost'],
db_user=installation_info['mysqlUser'],
db_password=installation_info['mysqlPassword'],
db_name=installation_info['mysqlDatabase'],
obstory_id=installation_info['observatoryId'])
logging.info("Starting satellite identification.")
# Count how many images we manage to successfully fit
outcomes = {
'successful_fits': 0,
'unsuccessful_fits': 0,
'error_records': 0,
'rescued_records': 0,
'insufficient_information': 0
}
# Status update
logging.info("Searching for satellites within period {} to {}".format(
date_string(utc_min), date_string(utc_max)))
# Open direct connection to database
conn = db.con
# Search for satellites within this time period
conn.execute(
"""
SELECT ao.obsTime, ao.publicId AS observationId, f.repositoryFname, l.publicId AS observatory
FROM archive_observations ao
LEFT OUTER JOIN archive_files f ON (ao.uid = f.observationId AND
f.semanticType=(SELECT uid FROM archive_semanticTypes WHERE name="pigazing:movingObject/video"))
INNER JOIN archive_observatories l ON ao.observatory = l.uid
INNER JOIN archive_metadata am2 ON ao.uid = am2.observationId AND
am2.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="web:category")
WHERE ao.obsType=(SELECT uid FROM archive_semanticTypes WHERE name='pigazing:movingObject/') AND
ao.obsTime BETWEEN %s AND %s AND
(am2.stringValue='Plane' OR am2.stringValue='Satellite' OR am2.stringValue='Junk')
ORDER BY ao.obsTime
""", (utc_min, utc_max))
results = conn.fetchall()
# Display logging list of the images we are going to work on
logging.info("Estimating the identity of {:d} spacecraft.".format(
len(results)))
# Analyse each spacecraft in turn
for item_index, item in enumerate(results):
# Fetch metadata about this object, some of which might be on the file, and some on the observation
obs_obj = db.get_observation(observation_id=item['observationId'])
obs_metadata = {item.key: item.value for item in obs_obj.meta}
if item['repositoryFname']:
file_obj = db.get_file(repository_fname=item['repositoryFname'])
file_metadata = {item.key: item.value for item in file_obj.meta}
else:
file_metadata = {}
all_metadata = {**obs_metadata, **file_metadata}
# Check we have all required metadata
if 'pigazing:path' not in all_metadata:
logging.info(
"Cannot process <{}> due to inadequate metadata.".format(
item['observationId']))
continue
# Make ID string to prefix to all logging messages about this event
logging_prefix = "{date} [{obs}]".format(
date=date_string(utc=item['obsTime']), obs=item['observationId'])
# Project path from (x,y) coordinates into (RA, Dec)
projector = PathProjection(db=db,
obstory_id=item['observatory'],
time=item['obsTime'],
logging_prefix=logging_prefix)
path_x_y, path_ra_dec_at_epoch, path_alt_az, sight_line_list_this = projector.ra_dec_from_x_y(
path_json=all_metadata['pigazing:path'],
path_bezier_json=all_metadata['pigazing:pathBezier'],
detections=all_metadata['pigazing:detectionCount'],
duration=all_metadata['pigazing:duration'])
# Check for error
if projector.error is not None:
if projector.error in outcomes:
outcomes[projector.error] += 1
continue
# Check for notifications
for notification in projector.notifications:
if notification in outcomes:
outcomes[notification] += 1
# Check number of points in path
path_len = len(path_x_y)
# Look up list of satellite orbital elements at the time of this sighting
spacecraft_list = fetch_satellites(utc=item['obsTime'])
# List of candidate satellites this object might be
candidate_satellites = []
# Check that we found a list of spacecraft
if spacecraft_list is None:
logging.info(
"{date} [{obs}] -- No spacecraft records found.".format(
date=date_string(utc=item['obsTime']),
obs=item['observationId']))
outcomes['insufficient_information'] += 1
continue
# Logging message about how many spacecraft we're testing
# logging.info("{date} [{obs}] -- Matching against {count:7d} spacecraft.".format(
# date=date_string(utc=item['obsTime']),
# obs=item['observationId'],
# count=len(spacecraft_list)
# ))
# Test for each candidate satellite in turn
for spacecraft in spacecraft_list:
# Unit scaling
deg2rad = pi / 180.0 # 0.0174532925199433
xpdotp = 1440.0 / (2.0 * pi) # 229.1831180523293
# Model the path of this spacecraft
model = Satrec()
model.sgp4init(
# whichconst: gravity model
WGS72,
# opsmode: 'a' = old AFSPC mode, 'i' = improved mode
'i',
# satnum: Satellite number
spacecraft['noradId'],
# epoch: days since 1949 December 31 00:00 UT
jd_from_unix(spacecraft['epoch']) - 2433281.5,
# bstar: drag coefficient (/earth radii)
spacecraft['bStar'],
# ndot (NOT USED): ballistic coefficient (revs/day)
spacecraft['meanMotionDot'] / (xpdotp * 1440.0),
# nddot (NOT USED): mean motion 2nd derivative (revs/day^3)
spacecraft['meanMotionDotDot'] / (xpdotp * 1440.0 * 1440),
# ecco: eccentricity
spacecraft['ecc'],
# argpo: argument of perigee (radians)
spacecraft['argPeri'] * deg2rad,
# inclo: inclination (radians)
spacecraft['incl'] * deg2rad,
# mo: mean anomaly (radians)
spacecraft['meanAnom'] * deg2rad,
# no_kozai: mean motion (radians/minute)
spacecraft['meanMotion'] / xpdotp,
# nodeo: right ascension of ascending node (radians)
spacecraft['RAasc'] * deg2rad)
# Wrap within skyfield to convert to topocentric coordinates
ts = load.timescale()
sat = EarthSatellite.from_satrec(model, ts)
# Fetch spacecraft position at each time point along trajectory
ang_mismatch_list = []
distance_list = []
# e, r, v = model.sgp4(jd_from_unix(utc=item['obsTime']), 0)
# logging.info("{} {} {}".format(str(e), str(r), str(v)))
tai_utc_offset = 39 # seconds
def satellite_angular_offset(index, clock_offset):
# Fetch observed position of object at this time point
pt_utc = path_x_y[index][3]
pt_alt = path_alt_az[index][0]
pt_az = path_alt_az[index][1]
# Project position of this satellite in space at this time point
t = ts.tai_jd(jd=jd_from_unix(utc=pt_utc + tai_utc_offset +
clock_offset))
# Project position of this satellite in the observer's sky
sight_line = sat - observer
topocentric = sight_line.at(t)
sat_alt, sat_az, sat_distance = topocentric.altaz()
# Work out offset of satellite's position from observed moving object
ang_mismatch = ang_dist(ra0=pt_az * pi / 180,
dec0=pt_alt * pi / 180,
ra1=sat_az.radians,
dec1=sat_alt.radians) * 180 / pi
return ang_mismatch, sat_distance
def time_offset_objective(p):
"""
Objective function that we minimise in order to find the best fit clock offset between the observed
and model paths.
:param p:
Vector with a single component: the clock offset
:return:
Metric to minimise
"""
# Turn input parameters into a time offset
clock_offset = p[0]
# Look up angular offset
ang_mismatch, sat_distance = satellite_angular_offset(
index=0, clock_offset=clock_offset)
# Return metric to minimise
return ang_mismatch * exp(clock_offset / 8)
# First, chuck out satellites with large angular offsets
observer = wgs84.latlon(
latitude_degrees=projector.obstory_info['latitude'],
longitude_degrees=projector.obstory_info['longitude'],
elevation_m=0)
ang_mismatch, sat_distance = satellite_angular_offset(
index=0, clock_offset=0)
# Check angular offset is reasonable
if ang_mismatch > global_settings['max_angular_mismatch']:
continue
# Work out the optimum time offset between the satellite's path and the observed path
# See <http://www.scipy-lectures.org/advanced/mathematical_optimization/>
# for more information about how this works
parameters_initial = [0]
parameters_optimised = scipy.optimize.minimize(
time_offset_objective,
np.asarray(parameters_initial),
options={
'disp': False,
'maxiter': 100
}).x
# Construct best-fit linear trajectory for best-fitting parameters
clock_offset = float(parameters_optimised[0])
# Check clock offset is reasonable
if abs(clock_offset) > global_settings['max_clock_offset']:
continue
# Measure the offset between the satellite's position and the observed position at each time point
for index in range(path_len):
# Look up angular mismatch at this time point
ang_mismatch, sat_distance = satellite_angular_offset(
index=index, clock_offset=clock_offset)
# Keep list of the offsets at each recorded time point along the trajectory
ang_mismatch_list.append(ang_mismatch)
distance_list.append(sat_distance.km)
# Consider adding this satellite to list of candidates
mean_ang_mismatch = np.mean(np.asarray(ang_mismatch_list))
distance_mean = np.mean(np.asarray(distance_list))
if mean_ang_mismatch < global_settings['max_mean_angular_mismatch']:
candidate_satellites.append({
'name':
spacecraft['name'], # string
'noradId':
spacecraft['noradId'], # int
'distance':
distance_mean, # km
'clock_offset':
clock_offset, # seconds
'offset':
mean_ang_mismatch, # degrees
'absolute_magnitude':
spacecraft['mag']
})
# Add model possibility for null satellite
candidate_satellites.append({
'name': "Unidentified",
'noradId': 0,
'distance': 35.7e3 *
0.25, # Nothing is visible beyond 25% of geostationary orbit distance
'clock_offset': 0,
'offset': 0,
'absolute_magnitude': None
})
# Sort candidates by score - use absolute mag = 20 for satellites with no mag
for candidate in candidate_satellites:
candidate['score'] = hypot(
candidate['distance'] / 1e3,
candidate['clock_offset'],
(20 if candidate['absolute_magnitude'] is None else
candidate['absolute_magnitude']),
)
candidate_satellites.sort(key=itemgetter('score'))
# Report possible satellite identifications
logging.info("{prefix} -- {satellites}".format(
prefix=logging_prefix,
satellites=", ".join([
"{} ({:.1f} deg offset; clock offset {:.1f} sec)".format(
satellite['name'], satellite['offset'],
satellite['clock_offset'])
for satellite in candidate_satellites
])))
# Identify most likely satellite
most_likely_satellite = candidate_satellites[0]
# Store satellite identification
user = settings['pigazingUser']
timestamp = time.time()
db.set_observation_metadata(user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(
key="satellite:name",
value=most_likely_satellite['name']))
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="satellite:norad_id",
value=most_likely_satellite['noradId']))
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="satellite:clock_offset",
value=most_likely_satellite['clock_offset']))
db.set_observation_metadata(user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(
key="satellite:angular_offset",
value=most_likely_satellite['offset']))
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="satellite:path_length",
value=ang_dist(ra0=path_ra_dec_at_epoch[0][0],
dec0=path_ra_dec_at_epoch[0][1],
ra1=path_ra_dec_at_epoch[-1][0],
dec1=path_ra_dec_at_epoch[-1][1]) *
180 / pi))
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(
key="satellite:path_ra_dec",
value="[[{:.3f},{:.3f}],[{:.3f},{:.3f}],[{:.3f},{:.3f}]]".
format(
path_ra_dec_at_epoch[0][0] * 12 / pi,
path_ra_dec_at_epoch[0][1] * 180 / pi,
path_ra_dec_at_epoch[int(path_len / 2)][0] * 12 / pi,
path_ra_dec_at_epoch[int(path_len / 2)][1] * 180 / pi,
path_ra_dec_at_epoch[-1][0] * 12 / pi,
path_ra_dec_at_epoch[-1][1] * 180 / pi,
)))
# Satellite successfully identified
if most_likely_satellite['name'] == "Unidentified":
outcomes['unsuccessful_fits'] += 1
else:
outcomes['successful_fits'] += 1
# Update database
db.commit()
# Report how many fits we achieved
logging.info("{:d} satellites successfully identified.".format(
outcomes['successful_fits']))
logging.info("{:d} satellites not identified.".format(
outcomes['unsuccessful_fits']))
logging.info("{:d} malformed database records.".format(
outcomes['error_records']))
logging.info("{:d} rescued database records.".format(
outcomes['rescued_records']))
logging.info("{:d} satellites with incomplete data.".format(
outcomes['insufficient_information']))
# Clean up and exit
db.commit()
db.close_db()
return
def calibrate_lens(obstory_id, utc_min, utc_max, utc_must_stop=None):
"""
Use astrometry.net to determine the orientation of a particular observatory.
:param obstory_id:
The ID of the observatory we want to determine the orientation for.
:param utc_min:
The start of the time period in which we should determine the observatory's orientation.
:param utc_max:
The end of the time period in which we should determine the observatory's orientation.
:param utc_must_stop:
The time by which we must finish work
:return:
None
"""
global parameter_scales, fit_list
# Open connection to database
[db0, conn] = connect_db.connect_db()
# Open connection to image archive
db = obsarchive_db.ObservationDatabase(
file_store_path=settings['dbFilestore'],
db_host=installation_info['mysqlHost'],
db_user=installation_info['mysqlUser'],
db_password=installation_info['mysqlPassword'],
db_name=installation_info['mysqlDatabase'],
obstory_id=installation_info['observatoryId'])
logging.info(
"Starting estimation of lens calibration for <{}>".format(obstory_id))
# Mathematical constants
deg = pi / 180
rad = 180 / pi
# Count how many successful fits we achieve
successful_fits = 0
# Read properties of known lenses
hw = hardware_properties.HardwareProps(
path=os.path.join(settings['pythonPath'], "..", "configuration_global",
"camera_properties"))
# Reduce time window to where observations are present
conn.execute(
"""
SELECT obsTime
FROM archive_observations
WHERE obsTime BETWEEN %s AND %s
AND observatory=(SELECT uid FROM archive_observatories WHERE publicId=%s)
ORDER BY obsTime ASC LIMIT 1
""", (utc_min, utc_max, obstory_id))
results = conn.fetchall()
if len(results) == 0:
logging.warning("No observations within requested time window.")
return
utc_min = results[0]['obsTime'] - 1
conn.execute(
"""
SELECT obsTime
FROM archive_observations
WHERE obsTime BETWEEN %s AND %s
AND observatory=(SELECT uid FROM archive_observatories WHERE publicId=%s)
ORDER BY obsTime DESC LIMIT 1
""", (utc_min, utc_max, obstory_id))
results = conn.fetchall()
utc_max = results[0]['obsTime'] + 1
# Divide up time interval into day-long blocks
logging.info("Searching for images within period {} to {}".format(
date_string(utc_min), date_string(utc_max)))
block_size = 3600
minimum_sky_clarity = 1e6 + 1400
utc_min = (floor(utc_min / block_size + 0.5) -
0.5) * block_size # Make sure that blocks start at noon
time_blocks = list(
np.arange(start=utc_min, stop=utc_max + block_size, step=block_size))
# Start new block whenever we have a hardware refresh
conn.execute(
"""
SELECT time FROM archive_metadata
WHERE observatory=(SELECT uid FROM archive_observatories WHERE publicId=%s)
AND fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey='refresh')
AND time BETWEEN %s AND %s
""", (obstory_id, utc_min, utc_max))
results = conn.fetchall()
for item in results:
time_blocks.append(item['time'])
# Make sure that start points for time blocks are in order
time_blocks.sort()
# Build list of images we are to analyse
images_for_analysis = []
for block_index, utc_block_min in enumerate(time_blocks[:-1]):
utc_block_max = time_blocks[block_index + 1]
logging.info("Calibrating lens within period {} to {}".format(
date_string(utc_block_min), date_string(utc_block_max)))
# Search for background-subtracted time lapse image with best sky clarity within this time period
conn.execute(
"""
SELECT ao.obsTime, ao.publicId AS observationId, f.repositoryFname, am.floatValue AS skyClarity
FROM archive_files f
INNER JOIN archive_observations ao on f.observationId = ao.uid
INNER JOIN archive_metadata am ON f.uid = am.fileId AND
am.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="pigazing:skyClarity")
LEFT OUTER JOIN archive_metadata am2 ON f.uid = am2.fileId AND
am2.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="calibration:lens_barrel_parameters")
WHERE ao.obsTime BETWEEN %s AND %s
AND ao.observatory=(SELECT uid FROM archive_observatories WHERE publicId=%s)
AND f.semanticType=(SELECT uid FROM archive_semanticTypes WHERE name="pigazing:timelapse/backgroundSubtracted")
AND am.floatValue > %s
AND am2.uid IS NULL
AND ao.astrometryProcessed IS NULL
ORDER BY am.floatValue DESC LIMIT 1
""", (utc_block_min, utc_block_max, obstory_id, minimum_sky_clarity))
results = conn.fetchall()
if len(results) > 0:
images_for_analysis.append({
'utc':
results[0]['obsTime'],
'skyClarity':
results[0]['skyClarity'],
'repositoryFname':
results[0]['repositoryFname'],
'observationId':
results[0]['observationId']
})
# Sort images into order of sky clarity
images_for_analysis.sort(key=itemgetter("skyClarity"))
images_for_analysis.reverse()
# Display logging list of the images we are going to work on
logging.info("Estimating the calibration of {:d} images:".format(
len(images_for_analysis)))
for item in images_for_analysis:
logging.info("{:17s} {:04.0f} {:32s}".format(date_string(item['utc']),
item['skyClarity'],
item['repositoryFname']))
# Analyse each image in turn
for item_index, item in enumerate(images_for_analysis):
logging.info("Working on image {:32s} ({:4d}/{:4d})".format(
item['repositoryFname'], item_index + 1, len(images_for_analysis)))
# Make a temporary directory to store files in.
# This is necessary as astrometry.net spams the cwd with lots of temporary junk
tmp0 = "/tmp/dcf21_calibrateLens_{}".format(item['repositoryFname'])
# logging.info("Created temporary directory <{}>".format(tmp))
os.system("mkdir {}".format(tmp0))
# 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_id=obstory_id,
time=item['utc'])
if not obstory_status:
logging.info("Aborting -- no observatory status available.")
continue
# Fetch observatory status
lens_name = obstory_status['lens']
lens_props = hw.lens_data[lens_name]
# 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 = lens_props.fov
# Find image orientation orientation
filename = os.path.join(settings['dbFilestore'],
item['repositoryFname'])
if not os.path.exists(filename):
logging.info("Error: File <{}> is missing!".format(
item['repositoryFname']))
continue
# 1. Copy image into working directory
# logging.info("Copying file")
img_name = item['repositoryFname']
command = "cp {} {}/{}_tmp.png".format(filename, tmp0, img_name)
# logging.info(command)
os.system(command)
# 2. We estimate the distortion of the image by passing a series of small portions of the image to
# astrometry.net. We use this to construct a mapping between (x, y) pixel coordinates to (RA, Dec).
# Define the size of each small portion we pass to astrometry.net
fraction_x = 0.15
fraction_y = 0.15
# Create a list of the centres of the portions we send
fit_list = []
portion_centres = [{'x': 0.5, 'y': 0.5}]
# Points along the leading diagonal of the image
for z in np.arange(0.1, 0.9, 0.1):
if z != 0.5:
portion_centres.append({'x': z, 'y': z})
portion_centres.append({'x': (z + 0.5) / 2, 'y': z})
portion_centres.append({'x': z, 'y': (z + 0.5) / 2})
# Points along the trailing diagonal of the image
for z in np.arange(0.1, 0.9, 0.1):
if z != 0.5:
portion_centres.append({'x': z, 'y': 1 - z})
portion_centres.append({'x': (1.5 - z) / 2, 'y': z})
portion_centres.append({'x': z, 'y': (1.5 - z) / 2})
# Points down the vertical centre-line of the image
for z in np.arange(0.15, 0.85, 0.1):
portion_centres.append({'x': 0.5, 'y': z})
# Points along the horizontal centre-line of the image
for z in np.arange(0.15, 0.85, 0.1):
portion_centres.append({'x': z, 'y': 0.5})
# Fetch the pixel dimensions of the image we are working on
d = image_dimensions("{}/{}_tmp.png".format(tmp0, img_name))
@dask.delayed
def analyse_image_portion(image_portion):
# Make a temporary directory to store files in.
# This is necessary as astrometry.net spams the cwd with lots of temporary junk
tmp = "/tmp/dcf21_calibrateLens_{}_{}".format(
item['repositoryFname'], image_portion['index'])
# logging.info("Created temporary directory <{}>".format(tmp))
os.system("mkdir {}".format(tmp))
# Use ImageMagick to crop out each small piece of the image
command = """
cd {6} ; \
rm -f {5}_tmp3.png ; \
convert {0}_tmp.png -colorspace sRGB -define png:format=png24 -crop {1:d}x{2:d}+{3:d}+{4:d} +repage {5}_tmp3.png
""".format(os.path.join(tmp0, img_name), int(fraction_x * d[0]),
int(fraction_y * d[1]),
int((image_portion['x'] - fraction_x / 2) * d[0]),
int((image_portion['y'] - fraction_y / 2) * d[1]),
img_name, tmp)
# logging.info(command)
os.system(command)
# Check that we've not run out of time
if utc_must_stop and (time.time() > utc_must_stop):
logging.info("We have run out of time! Aborting.")
os.system("rm -Rf {}".format(tmp))
return None
# How long should we allow astrometry.net to run for?
timeout = "40s"
# Run astrometry.net. Insert --no-plots on the command line to speed things up.
# logging.info("Running astrometry.net")
estimated_width = 2 * math.atan(
math.tan(estimated_image_scale / 2 * deg) * fraction_x) * rad
astrometry_output = os.path.join(tmp, "txt")
command = """
cd {5} ; \
timeout {0} solve-field --no-plots --crpix-center --scale-low {1:.1f} \
--scale-high {2:.1f} --overwrite {3}_tmp3.png > {4} 2> /dev/null \
""".format(timeout, estimated_width * 0.6, estimated_width * 1.2,
img_name, astrometry_output, tmp)
# logging.info(command)
os.system(command)
# Parse the output from astrometry.net
assert os.path.exists(
astrometry_output), "Path <{}> doesn't exist".format(
astrometry_output)
fit_text = open(astrometry_output).read()
# logging.info(fit_text)
# Clean up
# logging.info("Removing temporary directory <{}>".format(tmp))
os.system("rm -Rf {}".format(tmp))
# Extract celestial coordinates of the centre of the frame from astrometry.net output
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:
logging.info("FAIL(POS): Point ({:.2f},{:.2f}).".format(
image_portion['x'], image_portion['y']))
return None
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
# If astrometry.net achieved a fit, then we report it to the user
logging.info(
"FIT: RA: {:7.2f}h. Dec {:7.2f} deg. Point ({:.2f},{:.2f}).".
format(ra, dec, image_portion['x'], image_portion['y']))
# Also, populate <fit_list> with a list of the central points of the image fragments, and their (RA, Dec)
# coordinates.
return {
'ra': ra * pi / 12,
'dec': dec * pi / 180,
'x': image_portion['x'],
'y': image_portion['y'],
'radius': hypot(image_portion['x'] - 0.5,
image_portion['y'] - 0.5)
}
# Analyse each small portion of the image in turn
dask_tasks = []
for index, image_portion in enumerate(portion_centres):
image_portion['index'] = index
dask_tasks.append(
analyse_image_portion(image_portion=image_portion))
fit_list = dask.compute(*dask_tasks)
# Remove fits which returned None
fit_list = [i for i in fit_list if i is not None]
# Clean up
os.system("rm -Rf {}".format(tmp0))
os.system("rm -Rf /tmp/tmp.*")
# Make histogram of fits as a function of radius
radius_histogram = [0] * 10
for fit in fit_list:
radius_histogram[int(fit['radius'] * 10)] += 1
logging.info("Fit histogram vs radius: {}".format(radius_histogram))
# Reject this image if there are insufficient fits from astrometry.net
if min(radius_histogram[:5]) < 2:
logging.info("Insufficient fits to continue")
continue
# Fit a gnomonic projection to the image, with barrel correction, to fit all the celestial positions of the
# image fragments.
# See <http://www.scipy-lectures.org/advanced/mathematical_optimization/> for more information
ra0 = fit_list[0]['ra']
dec0 = fit_list[0]['dec']
parameter_scales = [
pi / 4, pi / 4, pi / 4, pi / 4, pi / 4, pi / 4, 5e-2, 5e-6
]
parameters_default = [
ra0, dec0, pi / 4, pi / 4, 0, lens_props.barrel_parameters[2], 0
]
parameters_initial = [
parameters_default[i] / parameter_scales[i]
for i in range(len(parameters_default))
]
fitting_result = scipy.optimize.minimize(mismatch,
parameters_initial,
method='nelder-mead',
options={
'xtol': 1e-8,
'disp': True,
'maxiter': 1e8,
'maxfev': 1e8
})
parameters_optimal = fitting_result.x
parameters_final = [
parameters_optimal[i] * parameter_scales[i]
for i in range(len(parameters_default))
]
# Display best fit numbers
headings = [["Central RA / hr", 12 / pi],
["Central Decl / deg", 180 / pi],
["Image width / deg", 180 / pi],
["Image height / deg", 180 / pi],
["Position angle / deg", 180 / pi], ["barrel_k1", 1],
["barrel_k2", 1]]
logging.info(
"Fit achieved to {:d} points with offset of {:.5f}. Best fit parameters were:"
.format(len(fit_list), fitting_result.fun))
for i in range(len(parameters_default)):
logging.info("{0:30s} : {1}".format(
headings[i][0], parameters_final[i] * headings[i][1]))
# Reject fit if objective function too large
if fitting_result.fun > 1e-4:
logging.info("Rejecting fit as chi-squared too large.")
continue
# Reject fit if k1/k2 values are too extreme
if (abs(parameters_final[5]) > 0.3) or (abs(parameters_final[6]) >
0.1):
logging.info("Rejecting fit as parameters seem extreme.")
continue
# Update observation status
successful_fits += 1
user = settings['pigazingUser']
timestamp = time.time()
barrel_parameters = [
parameters_final[2] * 180 / pi, parameters_final[3] * 180 / pi,
parameters_final[5], parameters_final[6], 0
]
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="calibration:lens_barrel_parameters",
value=json.dumps(barrel_parameters)))
db.set_observation_metadata(user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="calibration:chi_squared",
value=fitting_result.fun))
db.set_observation_metadata(user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="calibration:point_count",
value=str(radius_histogram)))
# Commit metadata changes
db.commit()
db0.commit()
# Report how many fits we achieved
logging.info(
"Total of {:d} images successfully fitted.".format(successful_fits))
if successful_fits > 0:
# Now determine mean lens calibration each day
logging.info("Averaging daily fits within period {} to {}".format(
date_string(utc_min), date_string(utc_max)))
block_size = 86400
utc_min = (floor(utc_min / block_size + 0.5) -
0.5) * block_size # Make sure that blocks start at noon
time_blocks = list(
np.arange(start=utc_min,
stop=utc_max + block_size,
step=block_size))
# Start new block whenever we have a hardware refresh
conn.execute(
"""
SELECT time FROM archive_metadata
WHERE observatory=(SELECT uid FROM archive_observatories WHERE publicId=%s)
AND fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey='refresh')
AND time BETWEEN %s AND %s
""", (obstory_id, utc_min, utc_max))
results = conn.fetchall()
for item in results:
time_blocks.append(item['time'])
# Make sure that start points for time blocks are in order
time_blocks.sort()
for block_index, utc_block_min in enumerate(time_blocks[:-1]):
utc_block_max = time_blocks[block_index + 1]
# Select observations with calibration fits
conn.execute(
"""
SELECT am1.stringValue AS barrel_parameters
FROM archive_observations o
INNER JOIN archive_metadata am1 ON o.uid = am1.observationId AND
am1.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="calibration:lens_barrel_parameters")
WHERE
o.observatory = (SELECT uid FROM archive_observatories WHERE publicId=%s) AND
o.obsTime BETWEEN %s AND %s;
""", (obstory_id, utc_block_min, utc_block_max))
results = conn.fetchall()
logging.info(
"Averaging fits within period {} to {}: Found {} fits.".format(
date_string(utc_block_min), date_string(utc_block_max),
len(results)))
# Average the fits we found
if len(results) < 3:
logging.info("Insufficient images to reliably average.")
continue
# Pick the median fit
value_list = {
'scale_x': [],
'scale_y': [],
'barrel_k1': [],
'barrel_k2': [],
'barrel_k3': []
}
for item in results:
barrel_parameters = json.loads(item['barrel_parameters'])
value_list['scale_x'].append(barrel_parameters[0])
value_list['scale_y'].append(barrel_parameters[1])
value_list['barrel_k1'].append(barrel_parameters[2])
value_list['barrel_k2'].append(barrel_parameters[3])
value_list['barrel_k3'].append(barrel_parameters[4])
median_values = {}
for key, values in value_list.items():
values.sort()
median_values[key] = values[len(values) // 2]
# Print fit information
logging.info("""\
CALIBRATION FIT from {:2d} images: %s. \
""".format(
len(results), "; ".join([
"{}: {}".format(key, median)
for key, median in median_values.items()
])))
# Flush any previous observation status
flush_calibration(obstory_id=obstory_id,
utc_min=utc_block_min - 1,
utc_max=utc_block_min + 1)
# Update observatory status
user = settings['pigazingUser']
timestamp = time.time()
barrel_parameters = [
median_values['scale_x'], median_values['scale_y'],
median_values['barrel_k1'], median_values['barrel_k2'],
median_values['barrel_k3']
]
db.register_obstory_metadata(
obstory_id=obstory_id,
key="calibration:lens_barrel_parameters",
value=json.dumps(barrel_parameters),
time_created=timestamp,
metadata_time=utc_block_min,
user_created=user)
db.commit()
# Clean up and exit
db.commit()
db.close_db()
db0.commit()
conn.close()
db0.close()
return
def list_calibration_fixes(obstory_id, utc_min, utc_max):
"""
List all the calibration fixes for a particular observatory.
:param obstory_id:
The ID of the observatory we want to list calibration fixes for.
:param utc_min:
The start of the time period in which we should list calibration fixes (unix time).
:param utc_max:
The end of the time period in which we should list calibration fixes (unix time).
:return:
None
"""
# Open connection to database
[db0, conn] = connect_db.connect_db()
# Start compiling list of calibration fixes
calibration_fixes = []
# Select observatory with calibration fits
conn.execute(
"""
SELECT am1.stringValue AS barrel_parameters,
am4.floatValue AS chi_squared, am5.stringValue AS point_count,
o.obsTime AS time
FROM archive_observations o
INNER JOIN archive_metadata am1 ON o.uid = am1.observationId AND
am1.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="calibration:lens_barrel_parameters")
INNER JOIN archive_metadata am4 ON o.uid = am4.observationId AND
am4.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="calibration:chi_squared")
INNER JOIN archive_metadata am5 ON o.uid = am5.observationId AND
am5.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="calibration:point_count")
WHERE
o.observatory = (SELECT uid FROM archive_observatories WHERE publicId=%s) AND
o.obsTime BETWEEN %s AND %s;
""", (obstory_id, utc_min, utc_max))
results = conn.fetchall()
for item in results:
calibration_fixes.append({
'time': item['time'],
'average': False,
'fit': item
})
# Select observation calibration fits
conn.execute(
"""
SELECT am1.stringValue AS barrel_parameters,
am3.floatValue AS chi_squared, am4.stringValue AS point_count,
am1.time AS time
FROM archive_observatories o
INNER JOIN archive_metadata am1 ON o.uid = am1.observatory AND
am1.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="calibration:lens_barrel_parameters")
LEFT OUTER JOIN archive_metadata am3 ON o.uid = am3.observatory AND am3.time=am1.time AND
am3.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="calibration:chi_squared")
LEFT OUTER JOIN archive_metadata am4 ON o.uid = am4.observatory AND am4.time=am1.time AND
am4.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="calibration:point_count")
WHERE
o.publicId=%s AND
am1.time BETWEEN %s AND %s;
""", (obstory_id, utc_min, utc_max))
results = conn.fetchall()
for item in results:
calibration_fixes.append({
'time': item['time'],
'average': True,
'fit': item
})
# Sort fixes by time
calibration_fixes.sort(key=itemgetter('time'))
# Display column headings
print("""\
{:1s} {:16s} {:8s} {:8s} {:10s} {:12s} {:6s}\
""".format("", "Time", "barrelK1", "barrelK2", "barrelK3", "chi2", "points"))
# Display fixes
for item in calibration_fixes:
# Deal with missing data
if item['fit']['chi_squared'] is None:
item['fit']['chi_squared'] = -1
if item['fit']['point_count'] is None:
item['fit']['point_count'] = "-"
# Display calibration fix
barrel_parameters = json.loads(item['fit']['barrel_parameters'])
print("""\
{:s} {:16s} {:8.4f} {:8.4f} {:10.7f} {:12.9f} {:s} {:s}\
""".format("\n>" if item['average'] else " ", date_string(item['time']),
barrel_parameters[2], barrel_parameters[3], barrel_parameters[4],
item['fit']['chi_squared'], item['fit']['point_count'],
"\n" if item['average'] else ""))
# Clean up and exit
return
def list_simultaneous_detections(utc_min=None, utc_max=None):
"""
Display a list of all the simultaneous object detections registered in the database.
:param utc_min:
Only show observations made after the specified time stamp.
:type utc_min:
float
:param utc_max:
Only show observations made before the specified time stamp.
:type utc_max:
float
:return:
None
"""
# Open connection to database
[db0, conn] = connect_db.connect_db()
# Compile search criteria for observation groups
where = [
"g.semanticType = (SELECT uid FROM archive_semanticTypes WHERE name=\"{}\")"
.format(simultaneous_event_type)
]
args = []
if utc_min is not None:
where.append("o.obsTime>=%s")
args.append(utc_min)
if utc_max is not None:
where.append("o.obsTime<=%s")
args.append(utc_max)
# Search for observation groups containing groups of simultaneous detections
conn.execute(
"""
SELECT g.publicId AS groupId, o.publicId AS obsId, o.obsTime, am.stringValue AS objectType
FROM archive_obs_groups g
INNER JOIN archive_obs_group_members m on g.uid = m.groupId
INNER JOIN archive_observations o ON m.childObservation = o.uid
INNER JOIN archive_metadata am ON g.uid = am.groupId AND
am.fieldId = (SELECT uid FROM archive_metadataFields WHERE metaKey="web:category")
WHERE """ + " AND ".join(where) + """
ORDER BY o.obsTime;
""", args)
results = conn.fetchall()
# Count how many simultaneous detections we find by type
detections_by_type = {}
# Compile list of groups
obs_groups = {}
obs_group_ids = []
for item in results:
key = item['groupId']
if key not in obs_groups:
obs_groups[key] = []
obs_group_ids.append({
'groupId': key,
'time': item['obsTime'],
'type': item['objectType']
})
# Add this simultaneous detection to tally
if item['objectType'] not in detections_by_type:
detections_by_type[item['objectType']] = 0
detections_by_type[item['objectType']] += 1
obs_groups[key].append(item['obsId'])
# List information about each observation in turn
print("{:16s} {:20s} {:20s} {:s}".format("Time", "groupId", "Object type",
"Observations"))
for group_info in obs_group_ids:
# Print group information
print("{:16s} {:20s} {:20s} {:s}".format(
group_info['groupId'], date_string(group_info['time']),
group_info['type'], " ".join(obs_groups[group_info['groupId']])))
# Report tally of events
print("\nTally of events by type:")
for event_type in sorted(detections_by_type.keys()):
print(" * {:26s}: {:6d}".format(event_type,
detections_by_type[event_type]))
def average_daily_fits(conn, db, obstory_id, utc_max, utc_min):
"""
Average all of the orientation fixes within a given time period, excluding extreme fits. Update the observatory's
status with a altitude and azimuth of the average fit, if it has a suitably small error bar.
:param conn:
Database connection object.
:param db:
Database object.
:param obstory_id:
Observatory publicId.
:param utc_max:
Unix time of the end of the time period.
:param utc_min:
Unix time of the beginning of the time period.
:return:
None
"""
# Divide up the time period in which we are working into individual nights, and then work on each night individually
logging.info("Averaging daily fits within period {} to {}".format(
date_string(utc_min), date_string(utc_max)))
# Each night is a 86400-second period
daily_block_size = 86400
# Make sure that blocks start at noon
utc_min = (floor(utc_min / daily_block_size + 0.5) -
0.5) * daily_block_size
time_blocks = list(
np.arange(start=utc_min,
stop=utc_max + daily_block_size,
step=daily_block_size))
# Start new block whenever we have a hardware refresh, even if it's in the middle of the night!
conn.execute(
"""
SELECT time FROM archive_metadata
WHERE observatory=(SELECT uid FROM archive_observatories WHERE publicId=%s)
AND fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey='refresh')
AND time BETWEEN %s AND %s
""", (obstory_id, utc_min, utc_max))
results = conn.fetchall()
for item in results:
time_blocks.append(item['time'])
# Make sure that start points for time blocks are in order
time_blocks.sort()
# Work on each time block (i.e. night) in turn
for block_index, utc_block_min in enumerate(time_blocks[:-1]):
# End point for this time block
utc_block_max = time_blocks[block_index + 1]
# Search for observations with orientation fits
conn.execute(
"""
SELECT am1.floatValue AS altitude, am2.floatValue AS azimuth, am3.floatValue AS pa, am4.floatValue AS tilt,
am5.floatValue AS width_x_field, am6.floatValue AS width_y_field,
am7.stringValue AS fit_quality
FROM archive_observations o
INNER JOIN archive_metadata am1 ON o.uid = am1.observationId AND
am1.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:altitude")
INNER JOIN archive_metadata am2 ON o.uid = am2.observationId AND
am2.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:azimuth")
INNER JOIN archive_metadata am3 ON o.uid = am3.observationId AND
am3.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:pa")
INNER JOIN archive_metadata am4 ON o.uid = am4.observationId AND
am4.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:tilt")
INNER JOIN archive_metadata am5 ON o.uid = am5.observationId AND
am5.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:width_x_field")
INNER JOIN archive_metadata am6 ON o.uid = am6.observationId AND
am6.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:width_y_field")
INNER JOIN archive_metadata am7 ON o.uid = am7.observationId AND
am7.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="orientation:fit_quality")
WHERE
o.observatory = (SELECT uid FROM archive_observatories WHERE publicId=%s) AND
o.obsTime BETWEEN %s AND %s;
""", (obstory_id, utc_block_min, utc_block_max))
results = conn.fetchall()
# Remove results with poor fit
results_filtered = []
fit_threshold = 2 # pixels
for item in results:
fit_quality = float(json.loads(item['fit_quality'])[0])
if fit_quality > fit_threshold:
continue
item['weight'] = 1 / (fit_quality + 0.1)
results_filtered.append(item)
results = results_filtered
# Report how many images we found
logging.info(
"Averaging fits within period {} to {}: Found {} fits.".format(
date_string(utc_block_min), date_string(utc_block_max),
len(results)))
# Average the fits we found
if len(results) < 4:
logging.info("Insufficient images to reliably average.")
continue
# What fraction of the worst fits do we reject?
rejection_fraction = 0.25
# Reject the 25% of fits which are further from the average
rejection_count = int(len(results) * rejection_fraction)
# Convert alt-az fits into radians and average
# Iteratively remove the point furthest from the mean
results_filtered = results
# Iteratively take the average of the fits, reject the furthest outlier, and then take a new average
for iteration in range(rejection_count):
# Average the (alt, az) measurements for this observatory by finding their centroid on a sphere
alt_az_list = [[i['altitude'] * deg, i['azimuth'] * deg]
for i in results_filtered]
weights_list = [i['weight'] for i in results_filtered]
alt_az_best = mean_angle_2d(pos_list=alt_az_list,
weights=weights_list)[0]
# Work out the offset of each fit from the average
fit_offsets = [
ang_dist(ra0=alt_az_best[1],
dec0=alt_az_best[0],
ra1=fitted_alt_az[1],
dec1=fitted_alt_az[0])
for fitted_alt_az in alt_az_list
]
# Reject the worst fit which is further from the average
fits_with_weights = list(zip(fit_offsets, results_filtered))
fits_with_weights.sort(key=operator.itemgetter(0))
fits_with_weights.reverse()
# Create a new list of orientation fits, with the worst outlier excluded
results_filtered = [item[1] for item in fits_with_weights[1:]]
# Convert alt-az fits into radians and average by finding their centroid on a sphere
alt_az_list = [[i['altitude'] * deg, i['azimuth'] * deg]
for i in results_filtered]
weights_list = [i['weight'] for i in results_filtered]
[alt_az_best, alt_az_error] = mean_angle_2d(pos_list=alt_az_list,
weights=weights_list)
# Average other angles by finding their centroid on a circle
output_values = {}
for quantity in ['tilt', 'pa', 'width_x_field', 'width_y_field']:
# Iteratively remove the point furthest from the mean
results_filtered = results
# Iteratively take the average of the values for each parameter, reject the furthest outlier,
# and then take a new average
for iteration in range(rejection_count):
# Average quantity measurements
quantity_values = [i[quantity] * deg for i in results_filtered]
weights_list = [i['weight'] for i in results_filtered]
quantity_mean = mean_angle(angle_list=quantity_values,
weights=weights_list)[0]
# Work out the offset of each fit from the average
fit_offsets = []
for index, quantity_value in enumerate(quantity_values):
offset = quantity_value - quantity_mean
if offset < -pi:
offset += 2 * pi
if offset > pi:
offset -= 2 * pi
fit_offsets.append(abs(offset))
# Reject the worst fit which is furthest from the average
fits_with_weights = list(zip(fit_offsets, results_filtered))
fits_with_weights.sort(key=operator.itemgetter(0))
fits_with_weights.reverse()
results_filtered = [item[1] for item in fits_with_weights[1:]]
# Filtering finished; now convert each fit into radians and average
values_filtered = [i[quantity] * deg for i in results_filtered]
weights_list = [i['weight'] for i in results_filtered]
value_best = mean_angle(angle_list=values_filtered,
weights=weights_list)[0]
output_values[quantity] = value_best * rad
# Print fit information
success = (
alt_az_error * rad < 0.1
) # Only accept determinations with better precision than 0.1 deg
adjective = "SUCCESSFUL" if success else "REJECTED"
logging.info("""\
{} ORIENTATION FIT from {:2d} images: Alt: {:.2f} deg. Az: {:.2f} deg. PA: {:.2f} deg. \
ScaleX: {:.2f} deg. ScaleY: {:.2f} deg. Uncertainty: {:.2f} deg.\
""".format(adjective, len(results_filtered), alt_az_best[0] * rad,
alt_az_best[1] * rad, output_values['tilt'],
output_values['width_x_field'], output_values['width_y_field'],
alt_az_error * rad))
# Update observatory status
if success:
# Flush any previous observation status
flush_orientation(obstory_id=obstory_id,
utc_min=utc_block_min - 1,
utc_max=utc_block_min + 1)
user = settings['pigazingUser']
timestamp = time.time()
db.register_obstory_metadata(obstory_id=obstory_id,
key="orientation:altitude",
value=alt_az_best[0] * rad,
time_created=timestamp,
metadata_time=utc_block_min,
user_created=user)
db.register_obstory_metadata(obstory_id=obstory_id,
key="orientation:azimuth",
value=alt_az_best[1] * rad,
time_created=timestamp,
metadata_time=utc_block_min,
user_created=user)
db.register_obstory_metadata(obstory_id=obstory_id,
key="orientation:pa",
value=output_values['pa'],
time_created=timestamp,
metadata_time=utc_block_min,
user_created=user)
db.register_obstory_metadata(obstory_id=obstory_id,
key="orientation:tilt",
value=output_values['tilt'],
time_created=timestamp,
metadata_time=utc_block_min,
user_created=user)
db.register_obstory_metadata(obstory_id=obstory_id,
key="orientation:width_x_field",
value=output_values['width_x_field'],
time_created=timestamp,
metadata_time=utc_block_min,
user_created=user)
db.register_obstory_metadata(obstory_id=obstory_id,
key="orientation:width_y_field",
value=output_values['width_y_field'],
time_created=timestamp,
metadata_time=utc_block_min,
user_created=user)
db.register_obstory_metadata(obstory_id=obstory_id,
key="orientation:uncertainty",
value=alt_az_error * rad,
time_created=timestamp,
metadata_time=utc_block_min,
user_created=user)
db.register_obstory_metadata(obstory_id=obstory_id,
key="orientation:image_count",
value=len(results),
time_created=timestamp,
metadata_time=utc_block_min,
user_created=user)
db.commit()
def search_simultaneous_detections(utc_min, utc_max, utc_must_stop):
# Count how many simultaneous detections we discover
simultaneous_detections_by_type = {}
db = obsarchive_db.ObservationDatabase(file_store_path=settings['dbFilestore'],
db_host=installation_info['mysqlHost'],
db_user=installation_info['mysqlUser'],
db_password=installation_info['mysqlPassword'],
db_name=installation_info['mysqlDatabase'],
obstory_id=installation_info['observatoryId'])
# Search for moving objects within time span
search = mp.ObservationSearch(observation_type="pigazing:movingObject/",
time_min=utc_min,
time_max=utc_max,
limit=1000000)
events_raw = db.search_observations(search)
# Use only event descriptors, not other returned fields
events = events_raw['obs']
# Make a list of which events are already members of groups
events_used = [False] * len(events)
# Look up the categorisation of each event
for event in events:
event.category = db.get_observation_metadata(event.id, "web:category")
# Throw out junk events and unclassified events
events = [x for x in events if x.category is not None and x.category not in ('Junk', 'Bin')]
# Look up which pre-existing observation groups each event is in
for index, event in enumerate(events):
db.con.execute("""
SELECT COUNT(*)
FROM archive_obs_groups grp
WHERE grp.semanticType = (SELECT y.uid FROM archive_semanticTypes y WHERE y.name=%s) AND
EXISTS (SELECT 1 FROM archive_obs_group_members x
WHERE x.groupId=grp.uid AND
x.childObservation=(SELECT z.uid FROM archive_observations z WHERE z.publicId=%s));
""", (simultaneous_event_type, event.id))
if db.con.fetchone()['COUNT(*)'] > 0:
events_used[index] = True
# Sort event descriptors into chronological order
events.sort(key=lambda x: x.obs_time)
# Look up the duration of each event, and calculate its end time
for event in events:
duration = 0
for meta in event.meta:
if meta.key == "pigazing:duration":
duration = meta.value
event.duration = duration
event.obs_time_end = event.obs_time + duration
# Compile list of simultaneous object detections
groups = []
# Search for simultaneous object detections
for index in range(len(events)):
# If we have already put this event in another simultaneous detection, don't add it to others
if events_used[index]:
continue
# Look up time span of event
event = events[index]
obstory_id_list = [event.obstory_id] # List of all observatories which saw this event
utc_min = event.obs_time # Earliest start time of any of the events in this group
utc_max = event.obs_time_end # Latest end time of any of the events in this group
events_used[index] = True
prev_group_size = -1
group_members = [index]
# Most events must be seen within a maximum offset of 1 second at different stations.
# Planes are allowed an offset of up to 30 seconds due to their large parallax
search_margin = 60
match_margin = 30 if event.category == "Plane" else 1
# Search for other events which fall within the same time span
# Do this iteratively, as a preceding event can expand the end time of the group, and vice versa
while len(group_members) > prev_group_size:
prev_group_size = len(group_members)
# Search for events at earlier times, and then at later times
for search_direction in (-1, 1):
# Start from the reference event
candidate_index = index
# Step through other events, providing they're within range
while ((candidate_index >= 0) and
(candidate_index < len(events))):
# Fetch event record
candidate = events[candidate_index]
# Stop search if we've gone out of time range
if ((candidate.obs_time_end < utc_min - search_margin) or
(candidate.obs_time > utc_max + search_margin)):
break
# Check whether this is a simultaneous detection, with same categorisation
if ((not events_used[candidate_index]) and
(candidate.category == event.category) and
(candidate.obs_time < utc_max + match_margin) and
(candidate.obs_time_end > utc_min - match_margin)):
# Add this event to the group, and update time span of event
group_members.append(candidate_index)
utc_min = min(utc_min, candidate.obs_time)
utc_max = max(utc_max, candidate.obs_time_end)
# Compile a list of all the observatories which saw this event
if candidate.obstory_id not in obstory_id_list:
obstory_id_list.append(candidate.obstory_id)
# Record that we have added this event to a group
events_used[candidate_index] = True
# Step on to the next candidate event to add into group
candidate_index += search_direction
# We have found a coincident detection only if multiple observatories saw an event at the same time
if len(obstory_id_list) < 2:
continue
# Update tally of events by type
if event.category not in simultaneous_detections_by_type:
simultaneous_detections_by_type[event.category] = 0
simultaneous_detections_by_type[event.category] += 1
# Initialise maximum baseline between the stations which saw this objects
maximum_obstory_spacing = 0
# Work out locations of all observatories which saw this event
obstory_locs = []
for obstory_id in obstory_id_list:
obstory_info = db.get_obstory_from_id(obstory_id)
obstory_loc = Point.from_lat_lng(lat=obstory_info['latitude'],
lng=obstory_info['longitude'],
alt=0,
utc=(utc_min + utc_max) / 2
)
obstory_locs.append(obstory_loc)
# Check the distances between all pairs of observatories
pairs = [[obstory_locs[i], obstory_locs[j]]
for i in range(len(obstory_id_list))
for j in range(i + 1, len(obstory_id_list))
]
# Work out maximum baseline between the stations which saw this objects
for pair in pairs:
maximum_obstory_spacing = max(maximum_obstory_spacing,
abs(pair[0].displacement_vector_from(pair[1])))
# Create information about this simultaneous detection
groups.append({'time': (utc_min + utc_max) / 2,
'obstory_list': obstory_id_list,
'time_spread': utc_max - utc_min,
'geographic_spacing': maximum_obstory_spacing,
'category': event.category,
'observations': [{'obs': events[x]} for x in group_members],
'ids': [events[x].id for x in group_members]})
# Report individual events we found
for item in groups:
logging.info("""
{time} -- {count:3d} stations; max baseline {baseline:5.0f} m; time spread {spread:4.1f} sec; type <{category}>
""".format(time=dcf_ast.date_string(item['time']),
count=len(item['obstory_list']),
baseline=item['geographic_spacing'],
spread=item['time_spread'],
category=item['category']).strip())
# Report statistics on events we found
logging.info("{:6d} moving objects seen within this time period".
format(len(events_raw['obs'])))
logging.info("{:6d} moving objects rejected because they were unclassified".
format(len(events_raw['obs']) - len(events)))
logging.info("{:6d} simultaneous detections found.".
format(len(groups)))
# Report statistics by event type
logging.info("Tally of simultaneous detections by type:")
for event_type in sorted(simultaneous_detections_by_type.keys()):
logging.info(" * {:32s}: {:6d}".format(event_type, simultaneous_detections_by_type[event_type]))
# Record simultaneous event detections into the database
for item in groups:
# Create new observation group
group = db.register_obsgroup(title="Multi-station detection", user_id="system",
semantic_type=simultaneous_event_type,
obs_time=item['time'], set_time=time.time(),
obs=item['ids'])
# logging.info("Simultaneous detection at {time} by {count:3d} stations (time spread {spread:.1f} sec)".
# format(time=dcf_ast.date_string(item['time']),
# count=len(item['obstory_list']),
# spread=item['time_spread']))
# logging.info("Observation IDs: %s" % item['ids'])
# Register group metadata
timestamp = time.time()
db.set_obsgroup_metadata(user_id="system", group_id=group.id, utc=timestamp,
meta=mp.Meta(key="web:category", value=item['category']))
db.set_obsgroup_metadata(user_id="system", group_id=group.id, utc=timestamp,
meta=mp.Meta(key="simultaneous:time_spread", value=item['time_spread']))
db.set_obsgroup_metadata(user_id="system", group_id=group.id, utc=timestamp,
meta=mp.Meta(key="simulataneous:geographic_spread", value=item['geographic_spacing']))
# Commit changes
db.commit()
def observing_loop():
obstory_id = installation_info['observatoryId']
logging.info("Observatory controller launched")
# Fetch observatory status, e.g. location, etc
logging.info("Fetching observatory status")
latitude = known_observatories[obstory_id]['latitude']
longitude = known_observatories[obstory_id]['longitude']
altitude = 0
latest_position_update = 0
flag_gps = 0
# Make sure that observatory exists in the database
# Start main observing loop
while True:
# Get a new MySQL connection because old one may not be connected any longer
db = obsarchive_db.ObservationDatabase(
file_store_path=settings['dbFilestore'],
db_host=installation_info['mysqlHost'],
db_user=installation_info['mysqlUser'],
db_password=installation_info['mysqlPassword'],
db_name=installation_info['mysqlDatabase'],
obstory_id=installation_info['observatoryId'])
# Get a GPS fix on the current time and our location
gps_fix = get_gps_fix()
if gps_fix:
latitude = gps_fix['latitude']
longitude = gps_fix['longitude']
altitude = gps_fix['altitude']
flag_gps = 1
# Decide whether we should observe, or do some day-time maintenance tasks
logging.info("Observation controller considering what to do next.")
time_now = time.time()
# How far below the horizon do we require the Sun to be before we start observing?
angle_below_horizon = settings['sunRequiredAngleBelowHorizon']
sun_times_yesterday = sunset_times.sun_times(
unix_time=time_now - 3600 * 24,
longitude=longitude,
latitude=latitude,
angle_below_horizon=angle_below_horizon)
sun_times_today = sunset_times.sun_times(
unix_time=time_now,
longitude=longitude,
latitude=latitude,
angle_below_horizon=angle_below_horizon)
sun_times_tomorrow = sunset_times.sun_times(
unix_time=time_now + 3600 * 24,
longitude=longitude,
latitude=latitude,
angle_below_horizon=angle_below_horizon)
logging.info("Sunrise at {}".format(
dcf_ast.date_string(sun_times_yesterday[0])))
logging.info("Sunset at {}".format(
dcf_ast.date_string(sun_times_yesterday[2])))
logging.info("Sunrise at {}".format(
dcf_ast.date_string(sun_times_today[0])))
logging.info("Sunset at {}".format(
dcf_ast.date_string(sun_times_today[2])))
logging.info("Sunrise at {}".format(
dcf_ast.date_string(sun_times_tomorrow[0])))
logging.info("Sunset at {}".format(
dcf_ast.date_string(sun_times_tomorrow[2])))
sun_margin = settings['sunMargin']
# Calculate whether it's currently night time, and how long until the next sunrise
is_night_time = False
seconds_till_sunrise = 0
# Test whether it is night time is we are between yesterday's sunset and today's sunrise
if (time_now > sun_times_yesterday[2] + sun_margin) and (
time_now < sun_times_today[0] - sun_margin):
logging.info("""
It is night time. We are between yesterday's sunset and today's sunrise.
""".strip())
is_night_time = True
seconds_till_sunrise = sun_times_today[0] - time_now
# Test whether it is between yesterday's sunset and today's sunrise
elif (time_now > sun_times_yesterday[2]) and (time_now <
sun_times_today[0]):
next_observing_time = sun_times_yesterday[2] + sun_margin
next_observing_wait = next_observing_time - time_now
if next_observing_wait > 0:
logging.info("""
We are between yesterday's sunset and today's sunrise, but sun has recently set. \
Waiting {:.0f} seconds (until {}) to start observing.
""".format(next_observing_wait,
dcf_ast.date_string(next_observing_time)).strip())
db.commit()
db.close_db()
del db
time.sleep(next_observing_wait + 2)
continue
# Test whether it is night time, since we are between today's sunrise and tomorrow's sunset
elif (time_now > sun_times_today[2] + sun_margin) and (
time_now < sun_times_tomorrow[0] - sun_margin):
logging.info("""
It is night time. We are between today's sunset and tomorrow's sunrise.
""".strip())
is_night_time = True
seconds_till_sunrise = sun_times_tomorrow[0] - time_now
# Test whether we between today's sunset and tomorrow's sunrise
elif (time_now > sun_times_today[2]) and (time_now <
sun_times_tomorrow[0]):
next_observing_time = sun_times_today[2] + sun_margin
next_observing_wait = next_observing_time - time_now
if next_observing_time > 0:
logging.info("""
We are between today's sunset and tomorrow's sunrise, but sun has recently set. \
Waiting {:.0f} seconds (until {}) to start observing.
""".format(next_observing_wait,
dcf_ast.date_string(next_observing_time)).strip())
db.commit()
db.close_db()
del db
time.sleep(next_observing_wait + 2)
continue
# Calculate time until the next sunset
seconds_till_sunset = sun_times_yesterday[2] - time_now
if seconds_till_sunset < -sun_margin:
seconds_till_sunset = sun_times_today[2] - time_now
if seconds_till_sunset < -sun_margin:
seconds_till_sunset = sun_times_tomorrow[2] - time_now
# If sunset was well in the past, and sunrise is well in the future, we should observe!
minimum_time_worth_observing = 600
if is_night_time and (seconds_till_sunrise >
(sun_margin + minimum_time_worth_observing)):
# Check that observatory exists
check_observatory_exists(db_handle=db,
obs_id=obstory_id,
utc=time.time())
# Fetch updated observatory status
obstory_status = db.get_obstory_status(obstory_id=obstory_id)
# If we've not stored a GPS fix in the database within the past six hours, do so now
if flag_gps and (time.time() > latest_position_update + 6 * 3600):
latest_position_update = time.time()
db.register_obstory_metadata(
obstory_id=obstory_id,
key="latitude_gps",
value=latitude,
metadata_time=time.time(),
time_created=time.time(),
user_created=settings['pigazingUser'])
db.register_obstory_metadata(
obstory_id=obstory_id,
key="longitude_gps",
value=longitude,
metadata_time=time.time(),
time_created=time.time(),
user_created=settings['pigazingUser'])
db.register_obstory_metadata(
obstory_id=obstory_id,
key="altitude_gps",
value=altitude,
metadata_time=time.time(),
time_created=time.time(),
user_created=settings['pigazingUser'])
# Create clipping region mask file
mask_file = "/tmp/triggermask_%d.txt" % os.getpid()
open(mask_file, "w").write("\n\n".join([
"\n".join([("%d %d" % tuple(p)) for p in point_list])
for point_list in json.loads(obstory_status["clipping_region"])
]))
# Commit updates to the database
db.commit()
db.close_db()
del db
# Calculate how long to observe for
observing_duration = seconds_till_sunrise - sun_margin
# Do not record too much video in one file, as otherwise the file will be big
if not settings['realTime']:
observing_duration = min(observing_duration,
settings['videoMaxRecordTime'])
# Start observing run
t_stop = time_now + observing_duration
logging.info("""
Starting observing run until {} (running for {:.0f} seconds).
""".format(dcf_ast.date_string(t_stop), observing_duration).strip())
# Flick the relay to turn the camera on
relay_control.camera_on()
time.sleep(5)
logging.info("Camera has been turned on.")
# Observe! We use different binaries depending whether we're using a webcam-like camera,
# or a DSLR connected via gphoto2
time_key = datetime.datetime.utcnow().strftime('%Y%m%d%H%M%S')
# Work out which C binary we're using to do observing
if settings['realTime']:
output_argument = ""
if obstory_status["camera_type"] == "gphoto2":
binary = "realtimeObserve_dslr"
else:
binary = "realtimeObserve"
else:
output_argument = """ --output \"{}/raw_video/{}_{}\" """.format(
settings['dataPath'], time_key, obstory_id)
if settings['i_am_a_rpi']:
binary = "recordH264_openmax"
else:
binary = "recordH264_libav"
binary_full_path = "{path}{debug}/{binary}".format(
path=settings['binaryPath'],
debug="/debug" if settings['debug'] else "",
binary=binary)
cmd = """
timeout {timeout} \
{binary} --utc-stop {utc_stop:.1f} \
--obsid \"{obsid}\" \
--device \"{device}\" \
--fps {fps} \
--width {width:d} \
--height {height:d} \
--mask \"{mask_file}\" \
--latitude {latitude} \
--longitude {longitude} \
--flag-gps {flag_gps} \
--flag-upside-down {upside_down} \
{output_argument}
""".format(timeout=float(observing_duration + 300),
binary=binary_full_path,
utc_stop=float(t_stop),
obsid=obstory_id,
device=settings['videoDev'],
width=int(obstory_status['camera_width']),
height=int(obstory_status['camera_height']),
fps=float(obstory_status['camera_fps']),
mask_file=mask_file,
latitude=float(latitude),
longitude=float(longitude),
flag_gps=int(flag_gps),
upside_down=int(obstory_status['camera_upside_down']),
output_argument=output_argument).strip()
logging.info("Running command: {}".format(cmd))
os.system(cmd)
# Flick the relay to turn the camera off
relay_control.camera_off()
time.sleep(5)
logging.info("Camera has been turned off.")
# Snooze for up to 10 minutes; we may rerun observing tasks in a while if they ended prematurely
if time.time() < t_stop:
snooze_duration = float(min(t_stop - time.time(), 600))
logging.info(
"Snoozing for {:.0f} seconds".format(snooze_duration))
time.sleep(snooze_duration)
continue
# It is day time, so consider running day time tasks
# First, commit updates to the database
db.commit()
db.close_db()
del db
# Estimate roughly when we're next going to be able to observe (i.e. shortly after sunset)
next_observing_wait = seconds_till_sunset + sun_margin
# If we've got more than an hour, it's worth doing some day time tasks
# Do daytime tasks on a RPi only if we are doing real-time observation
if (next_observing_wait > 3600) and (settings['realTime']
or not settings['i_am_a_rpi']):
t_stop = time_now + next_observing_wait
logging.info("""
Starting daytime tasks until {} (running for {:.0f} seconds).
""".format(dcf_ast.date_string(t_stop), next_observing_wait).strip())
os.system("cd {} ; ./daytimeTasks.py --stop-by {}".format(
os.path.join(settings['pythonPath'], "observe"), t_stop))
# Snooze for up to 30 minutes; we may rerun daytime tasks in a while if they ended prematurely
if time.time() < t_stop:
snooze_duration = float(min(t_stop - time.time(), 1800))
logging.info(
"Snoozing for {:.0f} seconds".format(snooze_duration))
time.sleep(snooze_duration)
else:
if next_observing_wait < 0:
next_observing_wait = 0
next_observing_wait += 30
t_stop = time_now + next_observing_wait
logging.info("""
Not time to start observing yet, so sleeping until {} ({:.0f} seconds away).
""".format(dcf_ast.date_string(t_stop), next_observing_wait).strip())
time.sleep(next_observing_wait)
# Little snooze to prevent spinning around the loop
snooze_duration = float(10)
logging.info("Snoozing for {:.0f} seconds".format(snooze_duration))
time.sleep(snooze_duration)
def list_planes(obstory_id, utc_min, utc_max):
"""
List all the plane identifications for a particular observatory.
:param obstory_id:
The ID of the observatory we want to list identifications for.
:param utc_min:
The start of the time period in which we should list identifications (unix time).
:param utc_max:
The end of the time period in which we should list identifications (unix time).
:return:
None
"""
# Open connection to database
[db0, conn] = connect_db.connect_db()
# Start compiling list of plane identifications
plane_identifications = []
# Select moving objects with plane identifications
conn.execute(
"""
SELECT am1.stringValue AS call_sign, am2.floatValue AS ang_offset,
am3.floatValue AS clock_offset, am4.floatValue AS duration, am5.stringValue AS hex_ident,
am6.floatValue AS distance,
am7.stringValue AS operator, am8.stringValue AS model, am9.stringValue AS manufacturer,
o.obsTime AS time, o.publicId AS obsId
FROM archive_observations o
INNER JOIN archive_metadata am1 ON o.uid = am1.observationId AND
am1.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="plane:call_sign")
INNER JOIN archive_metadata am2 ON o.uid = am2.observationId AND
am2.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="plane:angular_offset")
INNER JOIN archive_metadata am3 ON o.uid = am3.observationId AND
am3.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="plane:clock_offset")
INNER JOIN archive_metadata am4 ON o.uid = am4.observationId AND
am4.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="pigazing:duration")
INNER JOIN archive_metadata am5 ON o.uid = am5.observationId AND
am5.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="plane:hex_ident")
LEFT JOIN archive_metadata am6 ON o.uid = am6.observationId AND
am6.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="plane:distance")
LEFT JOIN archive_metadata am7 ON o.uid = am7.observationId AND
am7.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="plane:operator")
LEFT JOIN archive_metadata am8 ON o.uid = am8.observationId AND
am8.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="plane:model")
LEFT JOIN archive_metadata am9 ON o.uid = am9.observationId AND
am9.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="plane:manufacturer")
WHERE
o.observatory = (SELECT uid FROM archive_observatories WHERE publicId=%s) AND
o.obsTime BETWEEN %s AND %s;
""", (obstory_id, utc_min, utc_max))
results = conn.fetchall()
for item in results:
plane_identifications.append({
'id': item['obsId'],
'time': item['time'],
'call_sign': item['call_sign'],
'ang_offset': item['ang_offset'],
'clock_offset': item['clock_offset'],
'duration': item['duration'],
'hex_ident': item['hex_ident'],
'distance': item['distance'],
'operator': item['operator'],
'model': item['model'],
'manufacturer': item['manufacturer']
})
# Sort identifications by time
plane_identifications.sort(key=itemgetter('time'))
# Display column headings
print("""\
{:16s} {:18s} {:18s} {:8s} {:10s} {:10s} {:10s} {:30s} {:30s} {:30s}\
""".format("Time", "Call sign", "Hex ident", "Duration", "Ang offset",
"Clock off", "Distance", "Operator", "Model", "Manufacturer"))
# Display list of meteors
for item in plane_identifications:
print("""\
{:16s} {:18s} {:18s} {:8.1f} {:10.1f} {:10.1f} {:10.1f} {:30s} {:30s} {:30s}\
""".format(date_string(item['time']), item['call_sign'], item['hex_ident'],
item['duration'], item['ang_offset'], item['clock_offset'],
item['distance'], item['operator'], item['model'],
item['manufacturer']))
# Clean up and exit
return
def plane_determination(utc_min, utc_max, source):
"""
Estimate the identity of aircraft observed between the unix times <utc_min> and <utc_max>.
:param utc_min:
The start of the time period in which we should determine the identity of aircraft (unix time).
:type utc_min:
float
:param utc_max:
The end of the time period in which we should determine the identity of aircraft (unix time).
:type utc_max:
float
:param source:
The source we should use for plane trajectories. Either 'adsb' or 'fr24'.
:type source:
str
:return:
None
"""
# Open connection to image archive
db = obsarchive_db.ObservationDatabase(
file_store_path=settings['dbFilestore'],
db_host=installation_info['mysqlHost'],
db_user=installation_info['mysqlUser'],
db_password=installation_info['mysqlPassword'],
db_name=installation_info['mysqlDatabase'],
obstory_id=installation_info['observatoryId'])
logging.info("Starting aircraft identification.")
# Count how many images we manage to successfully fit
outcomes = {
'successful_fits': 0,
'unsuccessful_fits': 0,
'error_records': 0,
'rescued_records': 0,
'insufficient_information': 0
}
# Status update
logging.info("Searching for aircraft within period {} to {}".format(
date_string(utc_min), date_string(utc_max)))
# Open direct connection to database
conn = db.con
# Search for planes and satellites within this time period
conn.execute(
"""
SELECT ao.obsTime, ao.publicId AS observationId, f.repositoryFname, l.publicId AS observatory
FROM archive_observations ao
LEFT OUTER JOIN archive_files f ON (ao.uid = f.observationId AND
f.semanticType=(SELECT uid FROM archive_semanticTypes WHERE name="pigazing:movingObject/video"))
INNER JOIN archive_observatories l ON ao.observatory = l.uid
INNER JOIN archive_metadata am2 ON ao.uid = am2.observationId AND
am2.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="web:category")
WHERE ao.obsType=(SELECT uid FROM archive_semanticTypes WHERE name='pigazing:movingObject/') AND
ao.obsTime BETWEEN %s AND %s AND
(am2.stringValue='Plane' OR am2.stringValue='Satellite' OR am2.stringValue='Junk')
ORDER BY ao.obsTime
""", (utc_min, utc_max))
results = conn.fetchall()
# Display logging list of the images we are going to work on
logging.info("Estimating the identity of {:d} aircraft.".format(
len(results)))
# Analyse each aircraft in turn
for item_index, item in enumerate(results):
# Fetch metadata about this object, some of which might be on the file, and some on the observation
obs_obj = db.get_observation(observation_id=item['observationId'])
obs_metadata = {item.key: item.value for item in obs_obj.meta}
if item['repositoryFname']:
file_obj = db.get_file(repository_fname=item['repositoryFname'])
file_metadata = {item.key: item.value for item in file_obj.meta}
else:
file_metadata = {}
all_metadata = {**obs_metadata, **file_metadata}
# Check we have all required metadata
if 'pigazing:path' not in all_metadata:
logging.info(
"Cannot process <{}> due to inadequate metadata.".format(
item['observationId']))
continue
# Make ID string to prefix to all logging messages about this event
logging_prefix = "{date} [{obs}]".format(
date=date_string(utc=item['obsTime']), obs=item['observationId'])
# Project path from (x,y) coordinates into (RA, Dec)
projector = PathProjection(db=db,
obstory_id=item['observatory'],
time=item['obsTime'],
logging_prefix=logging_prefix)
path_x_y, path_ra_dec_at_epoch, path_alt_az, sight_line_list = projector.ra_dec_from_x_y(
path_json=all_metadata['pigazing:path'],
path_bezier_json=all_metadata['pigazing:pathBezier'],
detections=all_metadata['pigazing:detectionCount'],
duration=all_metadata['pigazing:duration'])
# Check for error
if projector.error is not None:
if projector.error in outcomes:
outcomes[projector.error] += 1
continue
# Check for notifications
for notification in projector.notifications:
if notification in outcomes:
outcomes[notification] += 1
# Check number of points in path
path_len = len(path_x_y)
# Look up list of aircraft tracks at the time of this sighting
if source == 'adsb':
aircraft_list = fetch_planes_from_adsb(utc=item['obsTime'])
elif source == 'fr24':
aircraft_list = fetch_planes_from_fr24(utc=item['obsTime'])
else:
raise ValueError("Unknown source <{}>".format(source))
# List of aircraft this moving object might be
candidate_aircraft = []
# Check that we found a list of aircraft
if aircraft_list is None:
logging.info("{date} [{obs}] -- No aircraft records found.".format(
date=date_string(utc=item['obsTime']),
obs=item['observationId']))
outcomes['insufficient_information'] += 1
continue
# Logging message about how many aircraft we're testing
# logging.info("{date} [{obs}] -- Matching against {count:7d} aircraft.".format(
# date=date_string(utc=item['obsTime']),
# obs=item['observationId'],
# count=len(aircraft_list)
# ))
# Test for each candidate aircraft in turn
for aircraft in aircraft_list:
# Fetch aircraft position at each time point along trajectory
ang_mismatch_list = []
distance_list = []
altitude_list = []
def aircraft_angular_offset(index, clock_offset):
# Fetch observed position of object at this time point
pt_utc = sight_line_list[index]['utc']
observatory_position = sight_line_list[index]['obs_position']
observed_sight_line = sight_line_list[index]['line'].direction
# Project position of this aircraft in space at this time point
aircraft_position = path_interpolate(aircraft=aircraft,
utc=pt_utc + clock_offset)
if aircraft_position is None:
return np.nan, np.nan, np.nan
# Convert position to Cartesian coordinates
aircraft_point = Point.from_lat_lng(
lat=aircraft_position['lat'],
lng=aircraft_position['lon'],
alt=aircraft_position['altitude'] * feet,
utc=None)
# Work out offset of plane's position from observed moving object
aircraft_sight_line = aircraft_point.to_vector(
) - observatory_position.to_vector()
angular_offset = aircraft_sight_line.angle_with(
other=observed_sight_line) # degrees
distance = abs(aircraft_sight_line)
altitude = aircraft_position['altitude'] * feet
return angular_offset, distance, altitude
def time_offset_objective(p):
"""
Objective function that we minimise in order to find the best fit clock offset between the observed
and model paths.
:param p:
Vector with a single component: the clock offset
:return:
Metric to minimise
"""
# Turn input parameters into a time offset
clock_offset = p[0]
# Look up angular offset
ang_mismatch, distance, altitude = aircraft_angular_offset(
index=0, clock_offset=clock_offset)
# Return metric to minimise
return ang_mismatch * exp(clock_offset / 8)
# Work out the optimum time offset between the plane's path and the observed path
# See <http://www.scipy-lectures.org/advanced/mathematical_optimization/>
# for more information about how this works
parameters_initial = [0]
parameters_optimised = scipy.optimize.minimize(
time_offset_objective,
np.asarray(parameters_initial),
options={
'disp': False,
'maxiter': 100
}).x
# Construct best-fit linear trajectory for best-fitting parameters
clock_offset = float(parameters_optimised[0])
# Check clock offset is reasonable
if abs(clock_offset) > global_settings['max_clock_offset']:
continue
# Measure the offset between the plane's position and the observed position at each time point
for index in range(path_len):
# Look up angular mismatch at this time point
ang_mismatch, distance, altitude = aircraft_angular_offset(
index=index, clock_offset=clock_offset)
# Keep list of the offsets at each recorded time point along the trajectory
ang_mismatch_list.append(ang_mismatch)
distance_list.append(distance)
altitude_list.append(altitude)
# Consider adding this plane to list of candidates
mean_ang_mismatch = np.mean(
np.asarray(ang_mismatch_list)) # degrees
distance_mean = np.mean(np.asarray(distance_list)) # metres
altitude_mean = np.mean(np.asarray(altitude_list)) # metres
if mean_ang_mismatch < global_settings['max_mean_angular_mismatch']:
start_time = sight_line_list[0]['utc']
end_time = sight_line_list[-1]['utc']
start_point = path_interpolate(aircraft=aircraft,
utc=start_time + clock_offset)
end_point = path_interpolate(aircraft=aircraft,
utc=end_time + clock_offset)
candidate_aircraft.append({
'call_sign': aircraft['call_sign'], # string
'hex_ident': aircraft['hex_ident'], # string
'distance': distance_mean / 1e3, # km
'altitude': altitude_mean / 1e3, # km
'clock_offset': clock_offset, # seconds
'offset': mean_ang_mismatch, # degrees
'start_point': start_point,
'end_point': end_point
})
# Add model possibility for null aircraft
if len(candidate_aircraft) == 0:
candidate_aircraft.append({
'call_sign': "Unidentified",
'hex_ident': "Unidentified",
'distance': 0,
'altitude': 0,
'clock_offset': 0,
'offset': 0,
'start_point': None,
'end_point': None
})
# Sort candidates by score
for candidate in candidate_aircraft:
candidate['score'] = hypot(
candidate['offset'],
candidate['clock_offset'],
)
candidate_aircraft.sort(key=itemgetter('score'))
# Report possible satellite identifications
logging.info("{prefix} -- {aircraft}".format(
prefix=logging_prefix,
aircraft=", ".join([
"{} ({:.1f} deg offset; clock offset {:.1f} sec; distance {:.1f} km)"
.format(aircraft['call_sign'], aircraft['offset'],
aircraft['clock_offset'], aircraft['distance'])
for aircraft in candidate_aircraft
])))
# Identify most likely aircraft
most_likely_aircraft = candidate_aircraft[0]
# Fetch extra information about plane
plane_info = fetch_aircraft_data(
hex_ident=most_likely_aircraft['hex_ident'])
# Store aircraft identification
user = settings['pigazingUser']
timestamp = time.time()
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="plane:call_sign",
value=most_likely_aircraft['call_sign']))
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="plane:hex_ident",
value=most_likely_aircraft['hex_ident']))
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="plane:clock_offset",
value=most_likely_aircraft['clock_offset']))
db.set_observation_metadata(user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(
key="plane:angular_offset",
value=most_likely_aircraft['offset']))
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="plane:distance",
value=most_likely_aircraft['distance']))
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="plane:mean_altitude",
value=most_likely_aircraft['altitude']))
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="plane:path",
value=json.dumps([
most_likely_aircraft['start_point'],
most_likely_aircraft['end_point']
])))
db.set_observation_metadata(
user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="plane:path_length",
value=ang_dist(ra0=path_ra_dec_at_epoch[0][0],
dec0=path_ra_dec_at_epoch[0][1],
ra1=path_ra_dec_at_epoch[-1][0],
dec1=path_ra_dec_at_epoch[-1][1]) *
180 / pi))
aircraft_operator = ""
if 'operator' in plane_info and plane_info['operator']:
aircraft_operator = plane_info['operator']
elif 'owner' in plane_info and plane_info['owner']:
aircraft_operator = plane_info['owner']
db.set_observation_metadata(user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="plane:operator",
value=aircraft_operator))
db.set_observation_metadata(user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="plane:model",
value=plane_info.get(
'model', '')))
db.set_observation_metadata(user_id=user,
observation_id=item['observationId'],
utc=timestamp,
meta=mp.Meta(key="plane:manufacturer",
value=plane_info.get(
'manufacturername', '')))
# Aircraft successfully identified
if most_likely_aircraft['call_sign'] == "Unidentified":
outcomes['unsuccessful_fits'] += 1
else:
outcomes['successful_fits'] += 1
# Update database
db.commit()
# Report how many fits we achieved
logging.info("{:d} aircraft successfully identified.".format(
outcomes['successful_fits']))
logging.info("{:d} aircraft not identified.".format(
outcomes['unsuccessful_fits']))
logging.info("{:d} malformed database records.".format(
outcomes['error_records']))
logging.info("{:d} rescued database records.".format(
outcomes['rescued_records']))
logging.info("{:d} aircraft with incomplete data.".format(
outcomes['insufficient_information']))
# Clean up and exit
db.commit()
db.close_db()
return
def list_satellites(obstory_id, utc_min, utc_max):
"""
List all the satellite identifications for a particular observatory.
:param obstory_id:
The ID of the observatory we want to list identifications for.
:param utc_min:
The start of the time period in which we should list identifications (unix time).
:param utc_max:
The end of the time period in which we should list identifications (unix time).
:return:
None
"""
# Open connection to database
[db0, conn] = connect_db.connect_db()
# Start compiling list of satellite identifications
satellite_identifications = []
# Select moving objects with satellite identifications
conn.execute(
"""
SELECT am1.stringValue AS satellite_name, am2.floatValue AS ang_offset,
am3.floatValue AS clock_offset, am4.floatValue AS duration, am5.floatValue AS norad_id,
o.obsTime AS time, o.publicId AS obsId
FROM archive_observations o
INNER JOIN archive_metadata am1 ON o.uid = am1.observationId AND
am1.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="satellite:name")
INNER JOIN archive_metadata am2 ON o.uid = am2.observationId AND
am2.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="satellite:angular_offset")
INNER JOIN archive_metadata am3 ON o.uid = am3.observationId AND
am3.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="satellite:clock_offset")
INNER JOIN archive_metadata am4 ON o.uid = am4.observationId AND
am4.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="pigazing:duration")
INNER JOIN archive_metadata am5 ON o.uid = am5.observationId AND
am5.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="satellite:norad_id")
WHERE
o.observatory = (SELECT uid FROM archive_observatories WHERE publicId=%s) AND
o.obsTime BETWEEN %s AND %s;
""", (obstory_id, utc_min, utc_max))
results = conn.fetchall()
for item in results:
satellite_identifications.append({
'id':
item['obsId'],
'time':
item['time'],
'satellite_name':
item['satellite_name'],
'ang_offset':
item['ang_offset'],
'clock_offset':
item['clock_offset'],
'duration':
item['duration'],
'norad_id':
int(item['norad_id'])
})
# Sort identifications by time
satellite_identifications.sort(key=itemgetter('time'))
# Display column headings
print("""\
{:16s} {:7s} {:32s} {:26s} {:8s} {:10s} {:10s}\
""".format("Time", "NORAD", "ID", "Satellite", "Duration", "Ang offset",
"Clock offset"))
# Display list of meteors
for item in satellite_identifications:
print("""\
{:16s} {:7d} {:32s} {:26s} {:8.1f} {:10.1f} {:10.1f}\
""".format(
date_string(item['time']),
item['norad_id'],
item['id'],
item['satellite_name'],
item['duration'],
item['ang_offset'],
item['clock_offset'],
))
# Clean up and exit
return
def timelapse_movie(utc_min, utc_max, obstory, img_types, stride, label):
"""
Make a time lapse video of images registered in the database using the command line tool ffmpeg.
:param utc_min:
Only return observations made after the specified time stamp.
:type utc_min:
float
:param utc_max:
Only return observations made before the specified time stamp.
:type utc_max:
float
:param obstory:
The public id of the observatory we are to fetch observations from
:type obstory:
str
:param img_types:
Only return images with these semantic types
:type img_types:
list[str]
:param stride:
Only return every nth observation matching the search criteria
:type stride:
int
:return:
None
"""
# Temporary directory to hold the images we are going to show
pid = os.getpid()
tmp = os.path.join("/tmp", "dcf_movie_images_{:d}".format(pid))
os.system("mkdir -p {}".format(tmp))
file_list = fetch_images(utc_min=utc_min,
utc_max=utc_max,
obstory=obstory,
img_types=img_types,
stride=stride)
# Report how many files we found
print("Observatory <{}>".format(obstory))
print(" * {:d} matching files in time range {} --> {}".format(len(file_list),
dcf_ast.date_string(utc_min),
dcf_ast.date_string(utc_max)))
# Make list of the stitched files
filename_list = []
filename_format = "frame_{:d}_%08d.jpg".format(pid)
for counter, file_item in enumerate(file_list):
# Look up the date of this file
[year, month, day, h, m, s] = dcf_ast.inv_julian_day(dcf_ast.jd_from_unix(
utc=file_item['observation']['obsTime']
))
# Filename for stitched image
fn = filename_format % counter
# Make list of input files
input_files = [os.path.join(settings['dbFilestore'],
file_item[semanticType]['repositoryFname'])
for semanticType in img_types]
command = "\
convert {inputs} +append -gravity SouthWest -fill Red -pointsize 26 -font Ubuntu-Bold \
-annotate +16+10 '{date} - {label1} - {label2}' {output} \
".format(inputs=" ".join(input_files),
date="{:02d}/{:02d}/{:04d} {:02d}:{:02d}".format(day, month, year, h, m),
label1="Sky clarity: {}".format(" / ".join(["{:04.0f}".format(file_item[semanticType]['skyClarity'])
for semanticType in img_types])),
label2=label,
output=os.path.join(tmp, fn))
# print(command)
os.system(command)
filename_list.append(fn)
command_line = "cd {} ; ffmpeg -r 10 -i {} -codec:v libx264 {}".format(tmp , filename_format, "timelapse.mp4")
print(command_line)
os.system(command_line)
def list_meteors(obstory_id, utc_min, utc_max):
"""
List all the meteor identifications for a particular observatory.
:param obstory_id:
The ID of the observatory we want to list meteor identifications for.
:param utc_min:
The start of the time period in which we should list meteor identifications (unix time).
:param utc_max:
The end of the time period in which we should list meteor identifications (unix time).
:return:
None
"""
# Open connection to database
[db0, conn] = connect_db.connect_db()
# Start compiling list of meteor identifications
meteor_identifications = []
# Count how many meteors we find in each shower
meteor_count_by_shower = {}
# Select observations with orientation fits
conn.execute("""
SELECT am1.stringValue AS name, am2.floatValue AS radiant_offset,
o.obsTime AS time, o.publicId AS obsId
FROM archive_observations o
INNER JOIN archive_metadata am1 ON o.uid = am1.observationId AND
am1.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="shower:name")
INNER JOIN archive_metadata am2 ON o.uid = am2.observationId AND
am2.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="shower:radiant_offset")
WHERE
o.observatory = (SELECT uid FROM archive_observatories WHERE publicId=%s) AND
o.obsTime BETWEEN %s AND %s;
""", (obstory_id, utc_min, utc_max))
results = conn.fetchall()
for item in results:
meteor_identifications.append({
'id': item['obsId'],
'time': item['time'],
'shower': item['name'],
'offset': item['radiant_offset']
})
# Update tally of meteors
if item['name'] not in meteor_count_by_shower:
meteor_count_by_shower[item['name']] = 0
meteor_count_by_shower[item['name']] += 1
# Sort meteors by time
meteor_identifications.sort(key=itemgetter('time'))
# Display column headings
print("""\
{:16s} {:20s} {:20s} {:5s}\
""".format("Time", "ID", "Shower", "Offset"))
# Display list of meteors
for item in meteor_identifications:
print("""\
{:16s} {:20s} {:26s} {:5.1f}\
""".format(date_string(item['time']),
item['id'],
item['shower'],
item['offset']
))
# Report tally of meteors
logging.info("Tally of meteors by shower:")
for shower in sorted(meteor_count_by_shower.keys()):
logging.info(" * {:26s}: {:6d}".format(shower, meteor_count_by_shower[shower]))
# Clean up and exit
return
def shower_determination(utc_min, utc_max):
"""
Estimate the parent showers of all meteors observed between the unix times <utc_min> and <utc_max>.
:param utc_min:
The start of the time period in which we should determine the parent showers of meteors (unix time).
:type utc_min:
float
:param utc_max:
The end of the time period in which we should determine the parent showers of meteors (unix time).
:type utc_max:
float
:return:
None
"""
# Load list of meteor showers
shower_list = read_shower_list()
# Open connection to image archive
db = obsarchive_db.ObservationDatabase(file_store_path=settings['dbFilestore'],
db_host=installation_info['mysqlHost'],
db_user=installation_info['mysqlUser'],
db_password=installation_info['mysqlPassword'],
db_name=installation_info['mysqlDatabase'],
obstory_id=installation_info['observatoryId'])
logging.info("Starting meteor shower identification.")
# Count how many images we manage to successfully fit
outcomes = {
'successful_fits': 0,
'error_records': 0,
'rescued_records': 0,
'insufficient_information': 0
}
# Status update
logging.info("Searching for meteors within period {} to {}".format(date_string(utc_min), date_string(utc_max)))
# Open direct connection to database
conn = db.con
# Search for meteors within this time period
conn.execute("""
SELECT ao.obsTime, ao.publicId AS observationId, f.repositoryFname, l.publicId AS observatory
FROM archive_observations ao
LEFT OUTER JOIN archive_files f ON (ao.uid = f.observationId AND
f.semanticType=(SELECT uid FROM archive_semanticTypes WHERE name="pigazing:movingObject/video"))
INNER JOIN archive_observatories l ON ao.observatory = l.uid
INNER JOIN archive_metadata am2 ON ao.uid = am2.observationId AND
am2.fieldId=(SELECT uid FROM archive_metadataFields WHERE metaKey="web:category")
WHERE ao.obsType=(SELECT uid FROM archive_semanticTypes WHERE name='pigazing:movingObject/') AND
ao.obsTime BETWEEN %s AND %s AND
am2.stringValue = "Meteor"
ORDER BY ao.obsTime;
""", (utc_min, utc_max))
results = conn.fetchall()
# Display logging list of the images we are going to work on
logging.info("Estimating the parent showers of {:d} meteors.".format(len(results)))
# Count how many meteors we find in each shower
meteor_count_by_shower = {}
# Analyse each meteor in turn
for item_index, item in enumerate(results):
# Fetch metadata about this object, some of which might be on the file, and some on the observation
obs_obj = db.get_observation(observation_id=item['observationId'])
obs_metadata = {item.key: item.value for item in obs_obj.meta}
if item['repositoryFname']:
file_obj = db.get_file(repository_fname=item['repositoryFname'])
file_metadata = {item.key: item.value for item in file_obj.meta}
else:
file_metadata = {}
all_metadata = {**obs_metadata, **file_metadata}
# Check we have all required metadata
if 'pigazing:path' not in all_metadata:
logging.info("Cannot process <{}> due to inadequate metadata.".format(item['observationId']))
continue
# Make ID string to prefix to all logging messages about this event
logging_prefix = "{date} [{obs}]".format(
date=date_string(utc=item['obsTime']),
obs=item['observationId']
)
# Project path from (x,y) coordinates into (RA, Dec)
projector = PathProjection(
db=db,
obstory_id=item['observatory'],
time=item['obsTime'],
logging_prefix=logging_prefix
)
path_x_y, path_ra_dec_at_epoch, path_alt_az, sight_line_list_this = projector.ra_dec_from_x_y(
path_json=all_metadata['pigazing:path'],
path_bezier_json=all_metadata['pigazing:pathBezier'],
detections=all_metadata['pigazing:detectionCount'],
duration=all_metadata['pigazing:duration']
)
# Check for error
if projector.error is not None:
if projector.error in outcomes:
outcomes[projector.error] += 1
continue
# Check for notifications
for notification in projector.notifications:
if notification in outcomes:
outcomes[notification] += 1
# Check number of points in path
path_len = len(path_x_y)
# List of candidate showers this meteor might belong to
candidate_showers = []
# Test for each candidate meteor shower in turn
for shower in shower_list:
# Work out celestial coordinates of shower radiant in RA/Dec in hours/degs of epoch
radiant_ra_at_epoch, radiant_dec_at_epoch = ra_dec_from_j2000(ra0=shower['RA'],
dec0=shower['Decl'],
utc_new=item['obsTime'])
# Work out alt-az of the shower's radiant using known location of camera. Fits returned in degrees.
alt_az_pos = alt_az(ra=radiant_ra_at_epoch, dec=radiant_dec_at_epoch,
utc=item['obsTime'],
latitude=projector.obstory_info['latitude'],
longitude=projector.obstory_info['longitude'])
# Work out position of the Sun (J2000)
sun_ra_j2000, sun_dec_j2000 = sun_pos(utc=item['obsTime'])
# Work out position of the Sun (RA, Dec of epoch)
sun_ra_at_epoch, sun_dec_at_epoch = ra_dec_from_j2000(ra0=sun_ra_j2000, dec0=sun_dec_j2000,
utc_new=item['obsTime'])
# Offset from peak of shower
year = 365.2524
peak_offset = (sun_ra_at_epoch * 180 / 12. - shower['peak']) * year / 360 # days
while peak_offset < -year / 2:
peak_offset += year
while peak_offset > year / 2:
peak_offset -= year
start_offset = peak_offset + shower['start'] - 4
end_offset = peak_offset + shower['end'] + 4
# Estimate ZHR of shower at the time the meteor was observed
zhr = 0
if abs(peak_offset) < 2:
zhr = shower['zhr'] # Shower is within 2 days of maximum; use quoted peak ZHR value
if start_offset < 0 < end_offset:
zhr = max(zhr, 5) # Shower is not at peak, but is active; assume ZHR=5
# Correct hourly rate for the altitude of the shower radiant
hourly_rate = zhr * sin(alt_az_pos[0] * pi / 180)
# If hourly rate is zero, this shower is not active
if hourly_rate <= 0:
# logging.info("Meteor shower <{}> has zero rate".format(shower['name']))
continue
# Work out angular distance of meteor from radiant (radians)
path_radiant_sep = [ang_dist(ra0=pt[0], dec0=pt[1],
ra1=radiant_ra_at_epoch * pi / 12, dec1=radiant_dec_at_epoch * pi / 180)
for pt in path_ra_dec_at_epoch]
change_in_radiant_dist = path_radiant_sep[-1] - path_radiant_sep[0] # radians
# Reject meteors that travel *towards* the radiant
if change_in_radiant_dist < 0:
continue
# Convert path to Cartesian coordinates on a unit sphere
path_cartesian = [Vector.from_ra_dec(ra=ra * 12 / pi, dec=dec * 180 / pi)
for ra, dec in path_ra_dec_at_epoch]
# Work out cross product of first and last point, which is normal to path of meteors
first = path_cartesian[0]
last = path_cartesian[-1]
path_normal = first.cross_product(last)
# Work out angle of path normal to meteor shower radiant
radiant_cartesian = Vector.from_ra_dec(ra=radiant_ra_at_epoch, dec=radiant_dec_at_epoch)
theta = path_normal.angle_with(radiant_cartesian) # degrees
if theta > 90:
theta = 180 - theta
# What is the angular separation of the meteor's path's closest approach to the shower radiant?
radiant_angle = 90 - theta
# Work out likelihood metric that this meteor belongs to this shower
radiant_angle_std_dev = 2 # Allow 2 degree mismatch in radiant pos
likelihood = hourly_rate * scipy.stats.norm(loc=0, scale=radiant_angle_std_dev).pdf(radiant_angle)
# Store information about the likelihood this meteor belongs to this shower
candidate_showers.append({
'name': shower['name'],
'likelihood': likelihood,
'offset': radiant_angle,
'change_radiant_dist': change_in_radiant_dist,
'shower_rate': hourly_rate
})
# Add model possibility for sporadic meteor
hourly_rate = 5
likelihood = hourly_rate * (1. / 90.) # Mean value of Gaussian in range 0-90 degs
candidate_showers.append({
'name': "Sporadic",
'likelihood': likelihood,
'offset': 0,
'shower_rate': hourly_rate
})
# Renormalise likelihoods to sum to unity
sum_likelihood = sum(shower['likelihood'] for shower in candidate_showers)
for shower in candidate_showers:
shower['likelihood'] *= 100 / sum_likelihood
# Sort candidates by likelihood
candidate_showers.sort(key=itemgetter('likelihood'), reverse=True)
# Report possible meteor shower identifications
logging.info("{date} [{obs}] -- {showers}".format(
date=date_string(utc=item['obsTime']),
obs=item['observationId'],
showers=", ".join([
"{} {:.1f}% ({:.1f} deg offset)".format(shower['name'], shower['likelihood'], shower['offset'])
for shower in candidate_showers
])
))
# Identify most likely shower
most_likely_shower = candidate_showers[0]['name']
# Update tally of meteors
if most_likely_shower not in meteor_count_by_shower:
meteor_count_by_shower[most_likely_shower] = 0
meteor_count_by_shower[most_likely_shower] += 1
# Store meteor identification
user = settings['pigazingUser']
timestamp = time.time()
db.set_observation_metadata(user_id=user, observation_id=item['observationId'], utc=timestamp,
meta=mp.Meta(key="shower:name", value=most_likely_shower))
db.set_observation_metadata(user_id=user, observation_id=item['observationId'], utc=timestamp,
meta=mp.Meta(key="shower:radiant_offset", value=candidate_showers[0]['offset']))
db.set_observation_metadata(user_id=user, observation_id=item['observationId'], utc=timestamp,
meta=mp.Meta(key="shower:path_length",
value=ang_dist(ra0=path_ra_dec_at_epoch[0][0],
dec0=path_ra_dec_at_epoch[0][1],
ra1=path_ra_dec_at_epoch[-1][0],
dec1=path_ra_dec_at_epoch[-1][1]
) * 180 / pi
))
db.set_observation_metadata(user_id=user, observation_id=item['observationId'], utc=timestamp,
meta=mp.Meta(key="shower:path_ra_dec",
value="[[{:.3f},{:.3f}],[{:.3f},{:.3f}],[{:.3f},{:.3f}]]".format(
path_ra_dec_at_epoch[0][0] * 12 / pi,
path_ra_dec_at_epoch[0][1] * 180 / pi,
path_ra_dec_at_epoch[int(path_len / 2)][0] * 12 / pi,
path_ra_dec_at_epoch[int(path_len / 2)][1] * 180 / pi,
path_ra_dec_at_epoch[-1][0] * 12 / pi,
path_ra_dec_at_epoch[-1][1] * 180 / pi,
)
))
# Meteor successfully identified
outcomes['successful_fits'] += 1
# Update database
db.commit()
# Report how many fits we achieved
logging.info("{:d} meteors successfully identified.".format(outcomes['successful_fits']))
logging.info("{:d} malformed database records.".format(outcomes['error_records']))
logging.info("{:d} rescued database records.".format(outcomes['rescued_records']))
logging.info("{:d} meteors with incomplete data.".format(outcomes['insufficient_information']))
# Report tally of meteors
logging.info("Tally of meteors by shower:")
for shower in sorted(meteor_count_by_shower.keys()):
logging.info(" * {:32s}: {:6d}".format(shower, meteor_count_by_shower[shower]))
# Clean up and exit
db.commit()
db.close_db()
return
def list_observatory_status(utc_min, utc_max, obstory):
"""
List all the metadata updates posted by a particular observatory between two given unix times.
:param utc_min:
Only list metadata updates after the specified unix time
:param utc_max:
Only list metadata updates before the specified unix time
:param obstory:
ID of the observatory we are to list events from
:return:
None
"""
# Open connection to image archive
db = obsarchive_db.ObservationDatabase(file_store_path=settings['dbFilestore'],
db_host=installation_info['mysqlHost'],
db_user=installation_info['mysqlUser'],
db_password=installation_info['mysqlPassword'],
db_name=installation_info['mysqlDatabase'],
obstory_id=installation_info['observatoryId'])
try:
obstory_info = db.get_obstory_from_id(obstory_id=obstory)
except ValueError:
print("Unknown observatory <{}>. Run ./listObservatories.py to see a list of available observatories.".
format(obstory))
sys.exit(0)
title = "Observatory <{}>".format(obstory)
print("\n\n{}\n{}".format(title, "-" * len(title)))
search = mp.ObservatoryMetadataSearch(obstory_ids=[obstory], time_min=utc_min, time_max=utc_max)
data = db.search_obstory_metadata(search)
data = data['items']
data.sort(key=lambda x: x.time)
print(" * {:d} matching metadata items in time range {} --> {}".format(len(data),
dcf_ast.date_string(utc_min),
dcf_ast.date_string(utc_max)))
# Check which items remain current
refreshed = False
data.reverse()
keys_seen = []
for item in data:
# The magic metadata keyword "refresh" causes all older metadata to be superseded
if item.key == "refresh" and not refreshed:
item.still_current = True
refreshed = True
# If we don't have a later metadata update for the same keyword, then this metadata remains current
elif item.key not in keys_seen and not refreshed:
item.still_current = True
keys_seen.append(item.key)
# This metadata item has been superseded
else:
item.still_current = False
data.reverse()
# Display list of items
for item in data:
if item.still_current:
current_flag = "+"
else:
current_flag = " "
print(" * {} [ID {}] {} -- {:16s} = {}".format(current_flag, item.id, dcf_ast.date_string(item.time),
item.key, item.value))
