def update_docket_metadata(d: Docket, docket_data: Dict[str, Any]) -> Docket:
"""Update the Docket object with the data from Juriscraper.
Works on either docket history report or docket report (appellate
or district) results.
"""
d = update_case_names(d, docket_data["case_name"])
mark_ia_upload_needed(d, save_docket=False)
d.docket_number = docket_data["docket_number"] or d.docket_number
d.date_filed = docket_data.get("date_filed") or d.date_filed
d.date_last_filing = (
docket_data.get("date_last_filing") or d.date_last_filing
)
d.date_terminated = docket_data.get("date_terminated") or d.date_terminated
d.cause = docket_data.get("cause") or d.cause
d.nature_of_suit = docket_data.get("nature_of_suit") or d.nature_of_suit
d.jury_demand = docket_data.get("jury_demand") or d.jury_demand
d.jurisdiction_type = (
docket_data.get("jurisdiction") or d.jurisdiction_type
)
d.mdl_status = docket_data.get("mdl_status") or d.mdl_status
judges = get_candidate_judges(
docket_data.get("assigned_to_str"),
d.court_id,
docket_data.get("date_filed"),
)
if judges is not None and len(judges) == 1:
d.assigned_to = judges[0]
d.assigned_to_str = docket_data.get("assigned_to_str") or ""
judges = get_candidate_judges(
docket_data.get("referred_to_str"),
d.court_id,
docket_data.get("date_filed"),
)
if judges is not None and len(judges) == 1:
d.referred_to = judges[0]
d.referred_to_str = docket_data.get("referred_to_str") or ""
d.blocked, d.date_blocked = get_blocked_status(d)
return d
def parse_harvard_opinions(reporter, volume, make_searchable):
"""
Parse downloaded CaseLaw Corpus from internet archive and add them to our
database.
Optionally uses a reporter abbreviation to identify cases to download as
used by IA. (Ex. T.C. => tc)
Optionally uses a volume integer.
If neither is provided, code will cycle through all downloaded files.
:param volume: The volume (int) of the reporters (optional) (ex 10)
:param reporter: Reporter string as slugify'd (optional) (tc) for T.C.
:param make_searchable: Boolean to indicate saving to solr
:return: None
"""
if not reporter and volume:
logger.error("You provided a volume but no reporter. Exiting.")
return
for file_path in filepath_list(reporter, volume):
ia_download_url = "/".join(
["https://archive.org/download", file_path.split("/", 9)[-1]]
)
if OpinionCluster.objects.filter(
filepath_json_harvard=file_path
).exists():
logger.info("Skipping - already in system %s" % ia_download_url)
continue
try:
with open(file_path) as f:
data = json.load(f)
except ValueError:
logger.warning("Empty json: missing case at: %s" % ia_download_url)
continue
except Exception as e:
logger.warning("Unknown error %s for: %s" % (e, ia_download_url))
continue
cites = get_citations(data["citations"][0]["cite"])
if not cites:
logger.info(
"No citation found for %s." % data["citations"][0]["cite"]
)
continue
case_name = harmonize(data["name_abbreviation"])
case_name_short = cnt.make_case_name_short(case_name)
case_name_full = harmonize(data["name"])
citation = cites[0]
if skip_processing(citation, case_name, file_path):
continue
# TODO: Generalize this to handle all court types somehow.
court_id = match_court_string(
data["court"]["name"],
state=True,
federal_appeals=True,
federal_district=True,
)
soup = BeautifulSoup(data["casebody"]["data"], "lxml")
# Some documents contain images in the HTML
# Flag them for a later crawl by using the placeholder '[[Image]]'
judge_list = [
extract_judge_last_name(x.text) for x in soup.find_all("judges")
]
author_list = [
extract_judge_last_name(x.text) for x in soup.find_all("author")
]
# Flatten and dedupe list of judges
judges = ", ".join(
sorted(
list(
set(
itertools.chain.from_iterable(judge_list + author_list)
)
)
)
)
judges = titlecase(judges)
docket_string = (
data["docket_number"]
.replace("Docket No.", "")
.replace("Docket Nos.", "")
.strip()
)
short_fields = ["attorneys", "disposition", "otherdate", "seealso"]
long_fields = [
"syllabus",
"summary",
"history",
"headnotes",
"correction",
]
short_data = parse_extra_fields(soup, short_fields, False)
long_data = parse_extra_fields(soup, long_fields, True)
with transaction.atomic():
logger.info("Adding docket for: %s", citation.base_citation())
docket = Docket(
case_name=case_name,
case_name_short=case_name_short,
case_name_full=case_name_full,
docket_number=docket_string,
court_id=court_id,
source=Docket.HARVARD,
ia_needs_upload=False,
)
try:
with transaction.atomic():
docket.save()
except OperationalError as e:
if "exceeds maximum" in str(e):
docket.docket_number = (
"%s, See Corrections for full Docket Number"
% trunc(docket_string, length=5000, ellipsis="...")
)
docket.save()
long_data["correction"] = "%s <br> %s" % (
data["docket_number"],
long_data["correction"],
)
# Handle partial dates by adding -01v to YYYY-MM dates
date_filed, is_approximate = validate_dt(data["decision_date"])
logger.info("Adding cluster for: %s", citation.base_citation())
cluster = OpinionCluster(
case_name=case_name,
case_name_short=case_name_short,
case_name_full=case_name_full,
precedential_status="Published",
docket_id=docket.id,
source="U",
date_filed=date_filed,
date_filed_is_approximate=is_approximate,
attorneys=short_data["attorneys"],
disposition=short_data["disposition"],
syllabus=long_data["syllabus"],
summary=long_data["summary"],
history=long_data["history"],
other_dates=short_data["otherdate"],
cross_reference=short_data["seealso"],
headnotes=long_data["headnotes"],
correction=long_data["correction"],
judges=judges,
filepath_json_harvard=file_path,
)
cluster.save(index=False)
logger.info("Adding citation for: %s", citation.base_citation())
Citation.objects.create(
volume=citation.volume,
reporter=citation.reporter,
page=citation.page,
type=map_reporter_db_cite_type(
REPORTERS[citation.canonical_reporter][0]["cite_type"]
),
cluster_id=cluster.id,
)
new_op_pks = []
for op in soup.find_all("opinion"):
# This code cleans author tags for processing.
# It is particularly useful for identifiying Per Curiam
for elem in [op.find("author")]:
if elem is not None:
[x.extract() for x in elem.find_all("page-number")]
auth = op.find("author")
if auth is not None:
author_tag_str = titlecase(auth.text.strip(":"))
author_str = titlecase(
"".join(extract_judge_last_name(author_tag_str))
)
else:
author_str = ""
author_tag_str = ""
per_curiam = True if author_tag_str == "Per Curiam" else False
# If Per Curiam is True set author string to Per Curiam
if per_curiam:
author_str = "Per Curiam"
op_type = map_opinion_type(op.get("type"))
opinion_xml = str(op)
logger.info("Adding opinion for: %s", citation.base_citation())
op = Opinion(
cluster_id=cluster.id,
type=op_type,
author_str=author_str,
xml_harvard=opinion_xml,
per_curiam=per_curiam,
extracted_by_ocr=True,
)
# Don't index now; do so later if desired
op.save(index=False)
new_op_pks.append(op.pk)
if make_searchable:
add_items_to_solr.delay(new_op_pks, "search.Opinion")
logger.info("Finished: %s", citation.base_citation())
