def make_browse_product_label(
bundle_db: BundleDB,
browse_collection_lidvid: str,
browse_product_lidvid: str,
browse_file_basename: str,
bundle_lidvid: str,
verify: bool,
) -> bytes:
"""
Create the label text for the browse product having the given
LIDVID using the bundle database. If verify is True, verify the
label against its XML and Schematron schemas. Raise an exception
if either fails.
"""
product: Product = bundle_db.get_product(browse_product_lidvid)
if not isinstance(product, BrowseProduct):
raise TypeError(f"{product} is not a BrowseProduct.")
browse_product: BrowseProduct = product
fits_product_lidvid = browse_product.fits_product_lidvid
file: File = bundle_db.get_file(browse_file_basename,
browse_product_lidvid)
if not isinstance(file, BrowseFile):
raise TypeError(f"{file} is not a BrowseFile.")
browse_file: BrowseFile = file
collection: Collection = bundle_db.get_collection(browse_collection_lidvid)
if not isinstance(collection, OtherCollection):
raise TypeError(f"{collection} is not a OtherCollection.")
browse_collection: OtherCollection = collection
bundle = bundle_db.get_bundle(bundle_lidvid)
try:
label = (make_label({
"proposal_id": bundle.proposal_id,
"suffix": browse_collection.suffix,
"browse_lid": lidvid_to_lid(browse_product_lidvid),
"browse_vid": lidvid_to_vid(browse_product_lidvid),
"data_lidvid": fits_product_lidvid,
"browse_file_name": browse_file_basename,
"object_length": browse_file.byte_size,
}).toxml().encode())
except Exception as e:
raise LabelError(browse_product_lidvid, browse_file_basename) from e
return pretty_and_verify(label, verify)
def make_fits_product_label(
working_dir: str,
bundle_db: BundleDB,
collection_lidvid: str,
product_lidvid: str,
bundle_lidvid: str,
file_basename: str,
verify: bool,
use_mod_date_for_testing: bool = False,
) -> bytes:
try:
product = bundle_db.get_product(product_lidvid)
collection = bundle_db.get_collection(collection_lidvid)
if not isinstance(collection, OtherCollection):
raise TypeError(f"{collection} is not OtherCollection.")
instrument = collection.instrument
suffix = collection.suffix
# If a label is created for testing purpose to compare with pre-made XML
# we will use MOD_DATE_FOR_TESTESING as the modification date.
if not use_mod_date_for_testing:
# Get the date when the label is created
mod_date = get_current_date()
else:
mod_date = MOD_DATE_FOR_TESTESING
card_dicts = bundle_db.get_card_dictionaries(product_lidvid,
file_basename)
lookup = DictLookup(file_basename, card_dicts)
siblings = _directory_siblings(working_dir, bundle_db, product_lidvid)
hdu_lookups = _find_RAWish_lookups(bundle_db, product_lidvid,
file_basename, siblings)
shm_lookup = _find_SHMish_lookup(bundle_db, product_lidvid,
file_basename, siblings)
start_date_time, stop_date_time = get_start_stop_date_times(
hdu_lookups, shm_lookup)
exposure_duration = get_exposure_duration(hdu_lookups, shm_lookup)
start_stop_times = {
"start_date_time": start_date_time,
"stop_date_time": stop_date_time,
"exposure_duration": exposure_duration,
}
# Store start/stop time for each fits_product in fits_products table.
# The min/max will be pulled out for roll-up in data collection/bundle.
bundle_db.update_fits_product_time(product_lidvid, start_date_time,
stop_date_time)
hst_parameters = get_hst_parameters(hdu_lookups, shm_lookup)
bundle = bundle_db.get_bundle(bundle_lidvid)
proposal_id = bundle.proposal_id
investigation_area_name = mk_Investigation_Area_name(proposal_id)
investigation_area_lidvid = mk_Investigation_Area_lidvid(proposal_id)
bundle_db.create_context_product(investigation_area_lidvid,
"investigation")
bundle_db.create_context_product(instrument_host_lidvid(),
"instrument_host")
bundle_db.create_context_product(observing_system_lidvid(instrument),
"instrument")
# Fetch target identifications from db
target_id = shm_lookup["TARG_ID"]
target_identifications = bundle_db.get_target_identifications_based_on_id(
target_id)
# At this stage, target identifications should be in the db
if len(target_identifications) == 0:
raise ValueError("Target identification is not stored in db.")
target_identification_nodes: List[NodeBuilder] = []
target_identification_nodes = create_target_identification_nodes(
bundle_db, target_identifications, "data")
# Get wavelength
instrument_id = get_instrument_id(hdu_lookups, shm_lookup)
detector_ids = get_detector_ids(hdu_lookups, shm_lookup)
filter_name = get_filter_name(hdu_lookups, shm_lookup)
wavelength_range = wavelength_ranges(instrument_id, detector_ids,
filter_name)
bundle_db.update_wavelength_range(product_lidvid, wavelength_range)
# Get title
channel_id = get_channel_id(hdu_lookups, shm_lookup)
try:
titles = get_titles_format(instrument_id, channel_id, suffix)
product_title = titles[0] + "."
product_title = product_title.format(I=instrument_id + "/" +
channel_id,
F=file_basename,
P=proposal_id)
collection_title = titles[1] + "."
collection_title = collection_title.format(I=instrument_id + "/" +
channel_id,
F=file_basename,
P=proposal_id)
# save data/misc collection title to OtherCollection table
bundle_db.update_fits_product_collection_title(
collection_lidvid, collection_title)
except KeyError:
# If product_title doesn't exist in SUFFIX_TITLES, we use the
# following text as the product_title.
product_title = (
f"{instrument_id} data file {file_basename} " +
f"obtained by the HST Observing Program {proposal_id}.")
# Dictionary used for primary result summary
processing_level = get_processing_level(suffix, instrument_id,
channel_id)
primary_result_dict: Dict[str, Any] = {}
primary_result_dict["processing_level"] = processing_level
primary_result_dict["description"] = product_title
primary_result_dict["wavelength_range"] = wavelength_range
# Dictionary passed into templates. Use the same data dictionary for
# either data label template or misc label template
data_dict = {
"lid":
lidvid_to_lid(product_lidvid),
"vid":
lidvid_to_vid(product_lidvid),
"title":
product_title,
"mod_date":
mod_date,
"file_name":
file_basename,
"file_contents":
get_file_contents(bundle_db, card_dicts, instrument,
product_lidvid),
"Investigation_Area":
investigation_area(investigation_area_name,
investigation_area_lidvid, "data"),
"Observing_System":
observing_system(instrument),
"Time_Coordinates":
get_time_coordinates(start_stop_times),
"Target_Identification":
combine_nodes_into_fragment(target_identification_nodes),
"HST":
hst_parameters,
"Primary_Result_Summary":
primary_result_summary(primary_result_dict),
"Reference_List":
make_document_reference_list([instrument], "data"),
}
# Pass the data_dict to either data label or misc label based on
# collection_type
collection_type = get_collection_type(suffix, instrument_id,
channel_id)
if collection_type == "data":
label = make_data_label(data_dict).toxml().encode()
elif collection_type == "miscellaneous":
label = make_misc_label(data_dict).toxml().encode()
except AssertionError:
raise AssertionError(
f"{product_lidvid} has no target identifications stored in DB.")
except Exception as e:
print(str(e))
raise LabelError(product_lidvid, file_basename,
(lookup, hdu_lookups[0], shm_lookup)) from e
return pretty_and_verify(label, verify)