def download_cutouts(sbid, username, password, destination_dir, catalogue_query, do_cutouts, cutout_radius_degrees=0.1):
# 2) Use CASDA VO (secure) to query for the images associated with the given scheduling_block_id
print ("\n\n** Finding images and image cubes for scheduling block {} ... \n\n".format(sbid))
data_product_id_query = "select * from ivoa.obscore where obs_id = '" + str(
sbid) + "' and dataproduct_type = 'cube' and dataproduct_subtype in ('cont.restored.t0', 'spectral.restored.3d')"
filename = destination_dir + "image_cubes_" + str(sbid) + ".xml"
casda.sync_tap_query(data_product_id_query, filename, username, password)
image_cube_votable = parse(filename, pedantic=False)
results_array = image_cube_votable.get_table_by_id('results').array
service = 'cutout_service' if do_cutouts else 'async_service'
# 3) For each of the image cubes, query datalink to get the secure datalink details
print ("\n\n** Retrieving datalink for each image and image cube...\n\n")
authenticated_id_tokens = []
for image_cube_result in results_array:
image_cube_id = image_cube_result['obs_publisher_did'].decode('utf-8')
async_url, authenticated_id_token = casda.get_service_link_and_id(image_cube_id, username,
password,
service=service,
destination_dir=destination_dir)
if authenticated_id_token is not None and len(authenticated_id_tokens) < 10:
authenticated_id_tokens.append(authenticated_id_token)
if len(authenticated_id_tokens) == 0:
print ("No image cubes for scheduling_block_id " + str(sbid))
return 1
# Run the catalogue_query to find catalogue entries that are of interest
if do_cutouts:
print ("\n\n** Finding components in each image and image cube...\n\n")
filename = destination_dir + "catalogue_query_" + str(sbid) + ".xml"
casda.sync_tap_query(catalogue_query, filename, username, password)
catalogue_vo_table = parse(filename, pedantic=False)
catalogue_results_array = catalogue_vo_table.get_table_by_id('results').array
print ("\n\n** Found %d components...\n\n" % (len(catalogue_results_array)))
if len(catalogue_results_array) == 0:
print ("No catalogue entries matching the criteria found for scheduling_block_id " + str(sbid))
return 1
# For each source found in the catalogue query, create a position filter
pos_list = []
for entry in catalogue_results_array:
ra = entry['ra_deg_cont']
dec = entry['dec_deg_cont']
circle = "CIRCLE " + str(ra) + " " + str(dec) + " " + str(cutout_radius_degrees)
pos_list.append(circle)
# Generate cutouts from each image around each source
# where there is no overlap an error file is generated but can be ignored.
job_location = casda.create_async_soda_job(authenticated_id_tokens)
if do_cutouts:
casda.add_params_to_async_job(job_location, 'pos', pos_list)
job_status = casda.run_async_job(job_location)
print ('\nJob finished with status %s address is %s\n\n' % (job_status, job_location))
if job_status != 'ERROR':
casda.download_all(job_location, destination_dir)
return 0
def download_cutouts(sbid, username, password, destination_dir, num_channels, data_product_sub_type):
print ("\n\n** Finding images and image cubes for scheduling block {} ... \n\n".format(sbid))
sbid_multi_channel_query = "SELECT TOP 1000 * FROM ivoa.obscore where obs_id='" + str(sbid) \
+ "' and dataproduct_subtype='" + str(data_product_sub_type) \
+ "' and em_xel > 1 and dataproduct_type = 'cube'"
# create async TAP query and wait for query to complete
result_file_path = casda.async_tap_query(sbid_multi_channel_query, username, password, destination_dir)
image_cube_votable = parse(result_file_path, pedantic=False)
results_array = image_cube_votable.get_table_by_id('results').array
# 3) For each of the image cubes, query datalink to get the secure datalink details
print ("\n\n** Retrieving datalink for each image and image cube...\n\n")
authenticated_id_tokens = []
for image_cube_result in results_array:
image_cube_id = image_cube_result['obs_publisher_did'].decode('utf-8')
async_url, authenticated_id_token = casda.get_service_link_and_id(image_cube_id, username,
password,
service='cutout_service',
destination_dir=destination_dir)
if authenticated_id_token is not None:
authenticated_id_tokens.append([authenticated_id_token, image_cube_result])
if len(authenticated_id_tokens) == 0:
print ("No image cubes for scheduling_block_id " + str(sbid))
return 1
# For each image cube, slice by channels using num_channels specified by the user.
job_locations = []
for entry in authenticated_id_tokens:
auth_id_token = entry[0]
# get the image cube and number of channels
ic = entry[1]
channel_count = ic['em_xel']
channel_list = []
# wrap to max number of channels, if provided value exceeds channel count
if num_channels > channel_count:
num_channels = channel_count
# slice up cube into chunks using the number of channels as the size of each chunk
slices = math.ceil(channel_count / num_channels)
current_step = 1
for s in range(slices):
channel_list.append(str(current_step) + " " + str(num_channels * int(s+1)))
current_step = (num_channels * int(s+1)) + 1
# create job for given band params
job_location = casda.create_async_soda_job([auth_id_token])
casda.add_params_to_async_job(job_location, 'CHANNEL', channel_list)
job_locations.append(job_location)
# run all jobs and download
casda.run_async_jobs_and_download(job_locations, destination_dir)
return 0
def main():
args = parseargs()
password = casda.get_opal_password(args.opal_password, args.password_file)
# Change this to choose which environment to use, prod is the default
casda.use_at()
start = time.time()
if args.destination_directory is not None and not os.path.exists(
args.destination_directory):
os.makedirs(args.destination_directory)
# Read cube dimensions
cube_dim = get_dimensions(args.cubeid)
# print("DIM=", cube_dim)
# Generate random locations in the cutout
pos_params, band_params = generate_random_cutouts(args, cube_dim)
# print("POS=", pos_params)
# print("BAND=", band_params)
# Get access to the cube - sia call then datalink
async_url, authenticated_id_token = casda.get_service_link_and_id(
args.cubeid,
args.opal_username,
password,
destination_dir=args.destination_directory)
print(async_url, authenticated_id_token)
# Create a job to retrieve the cutouts
job_location = casda.create_async_soda_job([authenticated_id_token],
soda_url=async_url)
casda.add_params_to_async_job(job_location, 'POS', pos_params)
casda.add_params_to_async_job(job_location, 'BAND', band_params)
print('\n\n Job will have %d cutouts.\n\n' %
(len(pos_params) * len(band_params)))
# Run and time the job
run_start = time.time()
status = casda.run_async_job(job_location)
run_end = time.time()
print('Job finished with status %s in %.02f s\n\n' %
(status, run_end - run_start))
# Optionally download
print("Job result available at ", casda.get_results_page(job_location))
if args.download:
casda.download_all(job_location, args.destination_directory)
# Report
end = time.time()
print('#### Cutout processing completed at %s ####' %
(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end))))
print('Job was processed in %.02f s' % (run_end - run_start))
print('Full run took %.02f s' % (end - start))
return 0
def produce_cutouts(source_list, image_id, username, password,
destination_dir):
# Use CASDA VO (secure) to query for the images associated with the given scheduling_block_id
print("\n\n** Retreiving image details for %s ... \n\n" % image_id)
filename = destination_dir + str(image_id) + ".xml"
data_product_id_query = "select * from ivoa.obscore where obs_publisher_did = '" + image_id + \
"' and dataproduct_type = 'cube'"
casda.sync_tap_query(data_product_id_query,
filename,
username=username,
password=password)
image_cube_votable = votable.parse(filename, pedantic=False)
results_array = image_cube_votable.get_table_by_id('results').array
# For each of the image cubes, query datalink to get the secure datalink details
print("\n\n** Retrieving datalink for each image and image cube...\n\n")
authenticated_id_tokens = []
for image_cube_result in results_array:
image_cube_id = image_cube_result['obs_publisher_did'].decode('utf-8')
async_url, authenticated_id_token = casda.get_service_link_and_id(
image_cube_id,
username,
password,
service='cutout_service',
destination_dir=destination_dir)
if authenticated_id_token is not None:
authenticated_id_tokens.append(authenticated_id_token)
if len(authenticated_id_tokens) == 0:
print("No image cubes found")
return 1
# Create the async job
job_location = casda.create_async_soda_job(authenticated_id_tokens)
# For each entry in the results of the catalogue query, add the position filter as a parameter to the async job
cutout_filters = []
for sky_loc in source_list:
ra = sky_loc.ra.degree
dec = sky_loc.dec.degree
circle = "CIRCLE " + str(ra) + " " + str(dec) + " " + str(
cutout_radius_degrees)
cutout_filters.append(circle)
casda.add_params_to_async_job(job_location, 'pos', cutout_filters)
# Run the job
status = casda.run_async_job(job_location)
# Download all of the files, or alert if it didn't complete
if status == 'COMPLETED':
print("\n\n** Downloading results...\n\n")
casda.download_all(job_location, destination_dir)
else:
print("Job did not complete: Status was %s." % status)
return 1
return 0
def main():
args = parseargs()
password = casda.get_opal_password(args.opal_password, args.password_file)
# Change this to choose which environment to use, prod is the default
casda.use_at()
start = time.time()
if args.destination_directory is not None and not os.path.exists(args.destination_directory):
os.makedirs(args.destination_directory)
# Read cube dimensions
cube_dim = get_dimensions(args.cubeid)
# print("DIM=", cube_dim)
# Generate random locations in the cutout
pos_params, band_params = generate_random_cutouts(args, cube_dim)
# print("POS=", pos_params)
# print("BAND=", band_params)
# Get access to the cube - sia call then datalink
async_url, authenticated_id_token = casda.get_service_link_and_id(
args.cubeid,
args.opal_username,
password,
destination_dir=args.destination_directory)
print (async_url, authenticated_id_token)
# Create a job to retrieve the cutouts
job_location = casda.create_async_soda_job([authenticated_id_token], soda_url=async_url)
casda.add_params_to_async_job(job_location, 'POS', pos_params)
casda.add_params_to_async_job(job_location, 'BAND', band_params)
print ('\n\n Job will have %d cutouts.\n\n' % (len(pos_params)*len(band_params)))
# Run and time the job
run_start = time.time()
status = casda.run_async_job(job_location)
run_end = time.time()
print('Job finished with status %s in %.02f s\n\n' % (status, run_end - run_start))
# Optionally download
print ("Job result available at ", casda.get_results_page(job_location))
if args.download:
casda.download_all(job_location, args.destination_directory)
# Report
end = time.time()
print('#### Cutout processing completed at %s ####'
% (time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end))))
print('Job was processed in %.02f s' % (run_end - run_start))
print('Full run took %.02f s' % (end - start))
return 0
def produce_cutouts(source_list, image_id, username, password, destination_dir):
# Use CASDA VO (secure) to query for the images associated with the given scheduling_block_id
print ("\n\n** Retreiving image details for %s ... \n\n" % image_id)
filename = destination_dir + str(image_id) + ".xml"
data_product_id_query = "select * from ivoa.obscore where obs_publisher_did = '" + image_id + \
"' and dataproduct_type = 'cube'"
casda.sync_tap_query(data_product_id_query, filename, username=username, password=password)
image_cube_votable = votable.parse(filename, pedantic=False)
results_array = image_cube_votable.get_table_by_id('results').array
# For each of the image cubes, query datalink to get the secure datalink details
print ("\n\n** Retrieving datalink for each image and image cube...\n\n")
authenticated_id_tokens = []
for image_cube_result in results_array:
image_cube_id = image_cube_result['obs_publisher_did'].decode('utf-8')
async_url, authenticated_id_token = casda.get_service_link_and_id(image_cube_id, username,
password,
service='cutout_service',
destination_dir=destination_dir)
if authenticated_id_token is not None:
authenticated_id_tokens.append(authenticated_id_token)
if len(authenticated_id_tokens) == 0:
print ("No image cubes found")
return 1
# Create the async job
job_location = casda.create_async_soda_job(authenticated_id_tokens)
# For each entry in the results of the catalogue query, add the position filter as a parameter to the async job
cutout_filters = []
for sky_loc in source_list:
ra = sky_loc.ra.degree
dec = sky_loc.dec.degree
circle = "CIRCLE " + str(ra) + " " + str(dec) + " " + str(cutout_radius_degrees)
cutout_filters.append(circle)
casda.add_params_to_async_job(job_location, 'pos', cutout_filters)
# Run the job
status = casda.run_async_job(job_location)
# Download all of the files, or alert if it didn't complete
if status == 'COMPLETED':
print ("\n\n** Downloading results...\n\n")
casda.download_all(job_location, destination_dir)
else:
print ("Job did not complete: Status was %s." % status)
return 1
return 0
def extract_spectra(source_list, cutout_radius_degrees, opal_username,
opal_password, destination_directory):
"""
Extract spectra at the specified locations from ASKAP image cubes. Only cubes of subtype spectral.restored.3d will be
used in the extraction. Cubes that are either released or in a project that the opal user has pre-release access to
will be included.
:param source_list: The list of SkyCoord objects specifying source positions
:param cutout_radius_degrees: The number of degrees around the central point to extract or find.
:param opal_username: The user's user name on the ATNF's online proposal system OPAL (normally an email address)
:param opal_password: The user's OPAL password
:param destination_directory: The directory where the resulting files will be stored
:return: None
"""
# Build query to produce list of cubes for the sources.
pos_params = build_pos_criteria(source_list, cutout_radius_degrees)
# Run an immediate sia2 job to get the list of target cubes
votable = casda.find_images(pos_params, opal_username, opal_password)
table = votable.get_first_table()
authenticated_ids = []
for row in table.array:
# We are only interested in the restored spectral line cubes
if row['dataproduct_subtype'].decode() == 'spectral.restored.3d':
data_product_id = row['obs_publisher_did'].decode('utf-8')
async_url, authenticated_id_token = casda.get_service_link_and_id(
data_product_id,
opal_username,
opal_password,
service='spectrum_generation_service',
destination_dir=destination_directory)
authenticated_ids.append(authenticated_id_token)
if len(authenticated_ids) == 0:
print(
'\n\nNo image cubes were found which matched any of your sources.')
return
# Generate spectra at each location for each cube - no spectra file is generated where there is no overlap
job_location = casda.create_async_soda_job(authenticated_ids)
casda.add_params_to_async_job(job_location, 'pos', pos_params)
job_status = casda.run_async_job(job_location)
print('\nJob finished with status %s address is %s\n\n' %
(job_status, job_location))
if job_status != 'ERROR':
casda.download_all(job_location, destination_directory)
def extract_spectra(source_list, cutout_radius_degrees, opal_username, opal_password, destination_directory):
"""
Extract spectra at the specified locations from ASKAP image cubes. Only cubes of subtype spectral.restored.3d will be
used in the extraction. Cubes that are either released or in a project that the opal user has pre-release access to
will be included.
:param source_list: The list of SkyCoord objects specifying source positions
:param cutout_radius_degrees: The number of degrees around the central point to extract or find.
:param opal_username: The user's user name on the ATNF's online proposal system OPAL (normally an email address)
:param opal_password: The user's OPAL password
:param destination_directory: The directory where the resulting files will be stored
:return: None
"""
# Build query to produce list of cubes for the sources.
pos_params = build_pos_criteria(source_list, cutout_radius_degrees)
# Run an immediate sia2 job to get the list of target cubes
votable = casda.find_images(pos_params, opal_username, opal_password)
table = votable.get_first_table()
authenticated_ids = []
for row in table.array:
# We are only interested in the restored spectral line cubes
if row['dataproduct_subtype'].decode() == 'spectral.restored.3d':
data_product_id = row['obs_publisher_did'].decode('utf-8')
async_url, authenticated_id_token = casda.get_service_link_and_id(data_product_id, opal_username,
opal_password,
service='spectrum_generation_service',
destination_dir=destination_directory)
authenticated_ids.append(authenticated_id_token)
if len(authenticated_ids) == 0:
print('\n\nNo image cubes were found which matched any of your sources.')
return
# Generate spectra at each location for each cube - no spectra file is generated where there is no overlap
job_location = casda.create_async_soda_job(authenticated_ids)
casda.add_params_to_async_job(job_location, 'pos', pos_params)
job_status = casda.run_async_job(job_location)
print('\nJob finished with status %s address is %s\n\n' % (job_status, job_location))
if job_status != 'ERROR':
casda.download_all(job_location, destination_directory)
def download_cutouts(sbid,
username,
password,
destination_dir,
catalogue_query,
do_cutouts,
cutout_radius_degrees=0.1):
# 2) Use CASDA VO (secure) to query for the images associated with the given scheduling_block_id
print(
"\n\n** Finding images and image cubes for scheduling block {} ... \n\n"
.format(sbid))
data_product_id_query = "select * from ivoa.obscore where obs_id = '" + str(
sbid
) + "' and dataproduct_type = 'cube' and dataproduct_subtype in ('cont.restored.t0', 'spectral.restored.3d')"
filename = destination_dir + "image_cubes_" + str(sbid) + ".xml"
casda.sync_tap_query(data_product_id_query, filename, username, password)
image_cube_votable = parse(filename, pedantic=False)
results_array = image_cube_votable.get_table_by_id('results').array
service = 'cutout_service' if do_cutouts else 'async_service'
# 3) For each of the image cubes, query datalink to get the secure datalink details
print("\n\n** Retrieving datalink for each image and image cube...\n\n")
authenticated_id_tokens = []
for image_cube_result in results_array:
image_cube_id = image_cube_result['obs_publisher_did'].decode('utf-8')
async_url, authenticated_id_token = casda.get_service_link_and_id(
image_cube_id,
username,
password,
service=service,
destination_dir=destination_dir)
if authenticated_id_token is not None and len(
authenticated_id_tokens) < 10:
authenticated_id_tokens.append(authenticated_id_token)
if len(authenticated_id_tokens) == 0:
print("No image cubes for scheduling_block_id " + str(sbid))
return 1
# Run the catalogue_query to find catalogue entries that are of interest
if do_cutouts:
print("\n\n** Finding components in each image and image cube...\n\n")
filename = destination_dir + "catalogue_query_" + str(sbid) + ".xml"
casda.sync_tap_query(catalogue_query, filename, username, password)
catalogue_vo_table = parse(filename, pedantic=False)
catalogue_results_array = catalogue_vo_table.get_table_by_id(
'results').array
print("\n\n** Found %d components...\n\n" %
(len(catalogue_results_array)))
if len(catalogue_results_array) == 0:
print(
"No catalogue entries matching the criteria found for scheduling_block_id "
+ str(sbid))
return 1
# For each source found in the catalogue query, create a position filter
pos_list = []
for entry in catalogue_results_array:
ra = entry['ra_deg_cont']
dec = entry['dec_deg_cont']
circle = "CIRCLE " + str(ra) + " " + str(dec) + " " + str(
cutout_radius_degrees)
pos_list.append(circle)
# Generate cutouts from each image around each source
# where there is no overlap an error file is generated but can be ignored.
job_location = casda.create_async_soda_job(authenticated_id_tokens)
if do_cutouts:
casda.add_params_to_async_job(job_location, 'pos', pos_list)
job_status = casda.run_async_job(job_location)
print('\nJob finished with status %s address is %s\n\n' %
(job_status, job_location))
if job_status != 'ERROR':
casda.download_all(job_location, destination_dir)
return 0
def produce_cutouts(source_list, proj, username, password, destination_dir,
cutout_radius_degrees):
# Use CASDA VO (secure) to query for the images associated with the given scheduling_block_id
print("\n\n** Retreiving image details for %s ... \n\n" % proj)
filename = destination_dir + str(proj) + ".xml"
#Do initial filter of images, allow for 3 deg cone around position (get ASKAP image which is ~30 sq deg).
src_num = 0
for sky_loc in source_list:
src_num = src_num + 1
ra = sky_loc.ra.degree
dec = sky_loc.dec.degree
data_product_id_query = "select * from ivoa.obscore where obs_collection LIKE '%" + proj + \
"%' and dataproduct_subtype = 'cont.restored.t0' and pol_states = '/I/' and 1 = CONTAINS(POINT('ICRS',"+ str(ra) + ","+ str(dec) + "),s_region)"
casda.sync_tap_query(data_product_id_query,
filename,
username=username,
password=password)
image_cube_votable = votable.parse(filename, pedantic=False)
results_array = image_cube_votable.get_table_by_id('results').array
# For each of the image cubes, query datalink to get the secure datalink details
print(
"\n\n** Retrieving datalink for each image containing source number "
+ str(src_num) + " ...\n\n")
authenticated_id_tokens = []
for image_cube_result in results_array:
image_cube_id = image_cube_result['obs_publisher_did'].decode(
'utf-8')
async_url, authenticated_id_token = casda.get_service_link_and_id(
image_cube_id,
username,
password,
service='cutout_service',
destination_dir=destination_dir)
if authenticated_id_token is not None:
authenticated_id_tokens.append(authenticated_id_token)
if len(authenticated_id_tokens) == 0:
print("No image cubes found")
return 1
# Create the async job
job_location = casda.create_async_soda_job(authenticated_id_tokens)
# For each entry in the results of the catalogue query, add the position filter as a parameter to the async job
cutout_filters = []
circle = "CIRCLE " + str(ra) + " " + str(dec) + " " + str(
cutout_radius_degrees)
cutout_filters.append(circle)
casda.add_params_to_async_job(job_location, 'pos', cutout_filters)
# Run the job
status = casda.run_async_job(job_location)
# Download all of the files, or alert if it didn't complete
if status == 'COMPLETED':
print("\n\n** Downloading results...\n\n")
casda.download_all(job_location, destination_dir)
returnflag = 0
else:
print("Job did not complete: Status was %s." % status)
returnflag = 1
if returnflag == 0:
return 0
else:
return 1
def download_cutouts(sbid, username, password, destination_dir, num_channels, data_product_sub_type):
print ("\n\n** Finding images and image cubes for scheduling block {} ... \n\n".format(sbid))
sbid_multi_channel_query = "SELECT TOP 1000 * FROM ivoa.obscore where obs_id='" + str(sbid) \
+ "' and dataproduct_subtype='" + str(data_product_sub_type) \
+ "' and em_xel > 1 and dataproduct_type = 'cube'"
# create async TAP query and wait for query to complete
result_file_path = casda.async_tap_query(sbid_multi_channel_query, username, password, destination_dir)
image_cube_votable = parse(result_file_path, pedantic=False)
results_array = image_cube_votable.get_table_by_id('results').array
# 3) For each of the image cubes, query datalink to get the secure datalink details
print ("\n\n** Retrieving datalink for each image and image cube...\n\n")
authenticated_id_tokens = []
for image_cube_result in results_array:
image_cube_id = image_cube_result['obs_publisher_did'].decode('utf-8')
async_url, authenticated_id_token = casda.get_service_link_and_id(image_cube_id, username,
password,
service='cutout_service',
destination_dir=destination_dir)
if authenticated_id_token is not None:
authenticated_id_tokens.append([authenticated_id_token, image_cube_result])
if len(authenticated_id_tokens) == 0:
print ("No image cubes for scheduling_block_id " + str(sbid))
return 1
# For each image cube, slice by channels using num_channels specified by the user.
band_list = []
job_locations = []
for entry in authenticated_id_tokens:
auth_id_token = entry[0]
ic = entry[1]
em_xel = ic['em_xel']
em_min = ic['em_min'] * u.m
em_max = ic['em_max'] * u.m
min_freq = em_max.to(u.Hz, equivalencies=u.spectral())
max_freq = em_min.to(u.Hz, equivalencies=u.spectral())
step_size = num_channels
if step_size > em_xel:
step_size = em_xel
hz_per_channel = (max_freq - min_freq) / em_xel
pos = em_xel
channel_blocks = math.ceil(em_xel / num_channels)
for b in range(int(channel_blocks)):
f1 = get_freq_at_pos(pos, min_freq, hz_per_channel)
pos -= step_size
f2 = get_freq_at_pos(pos, min_freq, hz_per_channel)
pos -= 1 # do not overlap channels between image cubes
wavelength1 = f1.to(u.m, equivalencies=u.spectral())
wavelength2 = f2.to(u.m, equivalencies=u.spectral())
band = str(wavelength1.value) + " " + str(wavelength2.value)
band_list.append(band)
# create job for given band params
job_location = casda.create_async_soda_job([auth_id_token])
casda.add_params_to_async_job(job_location, 'BAND', band_list)
job_locations.append(job_location)
# run all jobs and download
casda.run_async_jobs_and_download(job_locations, destination_dir)
return 0