def test_get_time_coordinates(self) -> None:
db = create_bundle_db_in_memory()
db.create_tables()
fits_product_lidvid = "urn:nasa:pds:hst_13012:data_acs_raw:jbz504eoq_raw::2.3"
os_filepath = path_to_testfile("jbz504eoq_raw.fits")
populate_database_from_fits_file(db, os_filepath, fits_product_lidvid)
file_basename = basename(os_filepath)
card_dicts = db.get_card_dictionaries(fits_product_lidvid,
file_basename)
nb = get_time_coordinates(
get_start_stop_times(
DictLookup("test_get_time_coordinates", card_dicts)))
doc = xml.dom.getDOMImplementation().createDocument(None, None, None)
str: bytes = nb(doc).toxml().encode()
str = pretty_print(str)
expected = b"""<?xml version="1.0"?>
<Time_Coordinates>
<start_date_time>2012-09-27T20:23:28Z</start_date_time>
<stop_date_time>2012-09-27T20:27:58Z</stop_date_time>
</Time_Coordinates>
"""
self.assertEqual(expected, str)
def test_get_default_target(self) -> None:
fits_product_lidvid = "urn:nasa:pds:hst_13012:data_acs_raw:jbz504eoq_raw::2.3"
card_dicts = [{
"DATE-OBS": "2001-01-02",
"TIME-OBS": "08:20:00",
"EXPTIME": "1.0"
}]
nb = get_time_coordinates(
get_start_stop_times(
DictLookup("test_get_default_target", card_dicts)))
doc = xml.dom.getDOMImplementation().createDocument(None, None, None)
str: bytes = nb(doc).toxml().encode()
str = pretty_print(str)
expected = b"""<?xml version="1.0"?>
<Time_Coordinates>
<start_date_time>2001-01-02T08:20:00Z</start_date_time>
<stop_date_time>2001-01-02T08:20:01Z</stop_date_time>
</Time_Coordinates>
"""
self.assertEqual(expected, str)
def make_bundle_label(
bundle_db: BundleDB,
bundle_lidvid: str,
info: Citation_Information,
verify: bool,
use_mod_date_for_testing: bool = False,
) -> bytes:
"""
Create the label text for the bundle in the bundle database using
the database connection. If verify is True, verify the label
against its XML and Schematron schemas. Raise an exception if
either fails.
"""
bundle = bundle_db.get_bundle(bundle_lidvid)
proposal_id = bundle.proposal_id
def get_ref_type(collection: Collection) -> str:
ref_type = switch_on_collection_subtype(
collection,
"bundle_has_context_collection",
"bundle_has_document_collection",
"bundle_has_schema_collection",
"bundle_has_other_collection",
)
if ref_type == "bundle_has_other_collection":
collection_type = cast(OtherCollection, collection).prefix
ref_type = f"bundle_has_{collection_type}_collection"
return ref_type
reduced_collections = [
make_bundle_entry_member({
"collection_lidvid": collection.lidvid,
"ref_type": get_ref_type(collection),
}) for collection in bundle_db.get_bundle_collections(bundle.lidvid)
]
# Get the bundle title from part of CitationInformation description
title = (info.title + ", HST Cycle " + str(info.cycle) + " Program " +
str(info.propno) + ", " + info.publication_year + ".")
# Get the list of target identifications nodes for the collection
target_identifications = bundle_db.get_all_target_identification()
target_identification_nodes: List[NodeBuilder] = []
target_identification_nodes = create_target_identification_nodes(
bundle_db, target_identifications, "bundle")
# Get the investigation node for the collection
investigation_area_name = mk_Investigation_Area_name(proposal_id)
investigation_area_lidvid = mk_Investigation_Area_lidvid(proposal_id)
investigation_area_node = investigation_area(investigation_area_name,
investigation_area_lidvid,
"bundle")
# Get min start_time and max stop_time
start_time, stop_time = bundle_db.get_roll_up_time_from_db()
# Make sure start/stop time exists in db.
if start_time is None:
raise ValueError("Start time is not stored in FitsProduct table.")
if stop_time is None:
raise ValueError("Stop time is not stored in FitsProduct table.")
start_stop_times = {
"start_date_time": start_time,
"stop_date_time": stop_time,
}
time_coordinates_node = get_time_coordinates(start_stop_times)
# Dictionary used for primary result summary
primary_result_dict: Dict[str, Any] = {}
# Put dummy value in processing level, wait for update.
primary_result_dict["processing_level"] = "Raw"
instruments_list = bundle_db.get_instruments_of_the_bundle()
instruments = ", ".join(instruments_list).upper()
p_title = (f"{instruments} observations obtained by the HST " +
f"Observing Program {proposal_id}.")
primary_result_dict["description"] = p_title
# Get unique wavelength names for roll-up in bundle
wavelength_range = bundle_db.get_wavelength_range_from_db()
primary_result_dict["wavelength_range"] = wavelength_range
primary_result_summary_node = primary_result_summary(primary_result_dict)
# Get the observing system node for the bundle
observing_system_nodes: List[NodeBuilder] = [
observing_system(instrument) for instrument in instruments_list
]
context_node: List[NodeBuilder] = []
context_node = [
make_bundle_context_node(
time_coordinates_node,
primary_result_summary_node,
investigation_area_node,
observing_system_nodes,
target_identification_nodes,
)
]
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
try:
label = (make_label({
"bundle_lid":
lidvid_to_lid(bundle.lidvid),
"bundle_vid":
lidvid_to_vid(bundle.lidvid),
"proposal_id":
str(proposal_id),
"title":
title,
"Citation_Information":
make_citation_information(info, is_for_bundle=True),
"mod_date":
mod_date,
"Bundle_Member_Entries":
combine_nodes_into_fragment(reduced_collections),
"Context_Area":
combine_nodes_into_fragment(context_node),
"Reference_List":
make_document_reference_list(instruments_list, "bundle"),
}).toxml().encode())
except Exception as e:
raise LabelError(bundle.lidvid) from e
if label[:6] != b"<?xml ":
raise ValueError("Bundle label is not XML.")
return pretty_and_verify(label, verify)
def make_other_collection_label(
bundle_db: BundleDB,
info: Citation_Information,
collection_lidvid: str,
bundle_lidvid: str,
verify: bool,
mod_date: str,
) -> bytes:
"""
Create the label text for the document, browse, and data collection having
this LIDVID using the bundle database. If verify is True, verify the label
against its XML and Schematron schemas. Raise an exception if either
fails.
"""
# TODO this is sloppy; is there a better way?
products = bundle_db.get_collection_products(collection_lidvid)
record_count = len(products)
if record_count <= 0:
raise ValueError(f"{collection_lidvid} has no products.")
collection_lid = lidvid_to_lid(collection_lidvid)
collection_vid = lidvid_to_vid(collection_lidvid)
collection: Collection = bundle_db.get_collection(collection_lidvid)
proposal_id = bundle_db.get_bundle(bundle_lidvid).proposal_id
instruments = ",".join(bundle_db.get_instruments_of_the_bundle()).upper()
def make_ctxt_coll_title(_coll: Collection) -> NodeBuilder:
return make_context_collection_title(
{
"instrument": instruments,
"proposal_id": str(proposal_id),
}
)
def make_doc_coll_title(_coll: Collection) -> NodeBuilder:
return make_document_collection_title(
{
"instrument": instruments,
"proposal_id": str(proposal_id),
}
)
def make_sch_coll_title(_coll: Collection) -> NodeBuilder:
return make_schema_collection_title(
{
"instrument": instruments,
"proposal_id": str(proposal_id),
}
)
def make_other_coll_title(coll: Collection) -> NodeBuilder:
other_collection = cast(OtherCollection, coll)
if other_collection.prefix == "browse":
collection_title = (
f"{other_collection.prefix.capitalize()} "
+ f"collection of {other_collection.instrument.upper()} "
+ f"observations obtained from HST Observing Program {proposal_id}."
)
else:
# Get the data/misc collection title from db.
collection_title = str(other_collection.title)
return make_other_collection_title({"collection_title": collection_title})
title: NodeBuilder = switch_on_collection_subtype(
collection,
make_ctxt_coll_title,
make_doc_coll_title,
make_sch_coll_title,
make_other_coll_title,
)(collection)
inventory_name = get_collection_inventory_name(bundle_db, collection_lidvid)
# Properly assign collection type for Document, Browse, or Data collection.
# Context node only exists in Data collection label.
# Reference_List only exists in Data collection label.
context_node: List[NodeBuilder] = []
reference_list_node: List[NodeBuilder] = []
collection_type: str = ""
type_name = type(collection).__name__
if type_name == "DocumentCollection":
collection_type = "Document"
# For document collection, we need to add all handbooks in the csv but
# we won't create the label for it.
inst_list = bundle_db.get_instruments_of_the_bundle()
record_count += 2 * len(inst_list)
elif type_name == "OtherCollection":
collection_type = cast(OtherCollection, collection).prefix.capitalize()
suffix = cast(OtherCollection, collection).suffix
instrument = cast(OtherCollection, collection).instrument
# Roll-up (Context node) only exists in data collection
if collection_type == "Data":
# Get min start_time and max stop_time
start_time, stop_time = bundle_db.get_roll_up_time_from_db(suffix)
# Make sure start/stop time exists in db.
if start_time is None:
raise ValueError("Start time is not stored in FitsProduct table.")
if stop_time is None:
raise ValueError("Stop time is not stored in FitsProduct table.")
start_stop_times = {
"start_date_time": start_time,
"stop_date_time": stop_time,
}
time_coordinates_node = get_time_coordinates(start_stop_times)
# Dictionary used for primary result summary
primary_result_dict: Dict[str, Any] = {}
# Check if it's raw or calibrated image, we will update this later
processing_level = get_processing_level(
suffix=suffix, instrument_id=instrument.upper()
)
primary_result_dict["processing_level"] = processing_level
p_title = bundle_db.get_fits_product_collection_title(collection_lidvid)
primary_result_dict["description"] = p_title
# Get unique wavelength names for roll-up in data collection
wavelength_range = bundle_db.get_wavelength_range_from_db(suffix)
primary_result_dict["wavelength_range"] = wavelength_range
primary_result_summary_node = primary_result_summary(primary_result_dict)
# Get the list of target identifications nodes for the collection
target_identifications = bundle_db.get_all_target_identification()
target_identification_nodes: List[NodeBuilder] = []
target_identification_nodes = create_target_identification_nodes(
bundle_db, target_identifications, "collection"
)
# Get the investigation node for the collection
investigation_area_name = mk_Investigation_Area_name(proposal_id)
investigation_area_lidvid = mk_Investigation_Area_lidvid(proposal_id)
investigation_area_node = investigation_area(
investigation_area_name, investigation_area_lidvid, "collection"
)
# Get the observing system node for the collection
observing_system_node = observing_system(instrument)
context_node = [
make_collection_context_node(
time_coordinates_node,
primary_result_summary_node,
investigation_area_node,
observing_system_node,
target_identification_nodes,
)
]
# document reference list only exists in data collection
reference_list_node = [
make_document_reference_list([instrument], "collection")
]
try:
label = (
make_label(
{
"collection_lid": collection_lid,
"collection_vid": collection_vid,
"record_count": record_count,
"title": title,
"mod_date": mod_date,
"proposal_id": str(proposal_id),
"Citation_Information": make_citation_information(info),
"inventory_name": inventory_name,
"Context_Area": combine_nodes_into_fragment(context_node),
"collection_type": collection_type,
"Reference_List": combine_nodes_into_fragment(reference_list_node),
}
)
.toxml()
.encode()
)
except Exception as e:
raise LabelError(collection_lidvid) 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)