def get_docket_and_claims(docket_number, court, case_name, cookies, tags, q):
"""Get the docket report, claims history report, and save it all to the DB
and Solr
"""
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=docket_number,
court_id=court,
cookies=cookies,
case_name=case_name,
docket_number_letters="bk",
).set(queue=q),
get_docket_by_pacer_case_id.s(court_id=court,
cookies=cookies,
tag_names=tags,
**{
"show_parties_and_counsel": True,
"show_terminated_parties": True,
"show_list_of_member_cases": False,
}).set(queue=q),
get_bankr_claims_registry.s(
cookies=cookies,
tag_names=tags,
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def get_dockets(options, items, tags, sample_size=0, doc_num_end=""):
"""Download dockets from PACER.
:param options: Options provided by argparse
:param items: Items from our FJC IDB database
:param tags: A list of tag names to associate with the purchased content.
:param sample_size: The number of items to get. If 0, get them all. Else,
get only this many and do it randomly.
:param doc_num_end: Only get docket numbers up to this value to constrain
costs. If set to an empty string, no constraints are applied. Note that
applying this value means no unnumbered entries will be retrieved by PACER.
"""
if sample_size > 0:
items = items.order_by("?")[:sample_size]
q = options["queue"]
throttle = CeleryThrottle(queue_name=q)
session = PacerSession(username=PACER_USERNAME, password=PACER_PASSWORD)
session.login()
for i, row in enumerate(items):
if i < options["offset"]:
continue
if i >= options["limit"] > 0:
break
if i % 5000 == 0:
# Re-authenticate just in case the auto-login mechanism isn't
# working.
session = PacerSession(username=PACER_USERNAME,
password=PACER_PASSWORD)
session.login()
# All tests pass. Get the docket.
logger.info("Doing row %s: %s", i, row)
throttle.maybe_wait()
params = make_fjc_idb_lookup_params(row)
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=row.docket_number,
court_id=row.district_id,
cookies=session.cookies,
**params,
).set(queue=q),
filter_docket_by_tags.s(tags, row.district_id).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id=row.district_id,
cookies=session.cookies,
tag_names=tags,
**{
"show_parties_and_counsel": True,
"show_terminated_parties": True,
"show_list_of_member_cases": False,
"doc_num_end": doc_num_end,
},
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def get_dockets(options, items, tags, sample_size=0, doc_num_end=''):
"""Download dockets from PACER.
:param options: Options provided by argparse
:param items: Items from our FJC IDB database
:param tags: A list of tag names to associate with the purchased content.
:param sample_size: The number of items to get. If 0, get them all. Else,
get only this many and do it randomly.
:param doc_num_end: Only get docket numbers up to this value to constrain
costs. If set to an empty string, no constraints are applied. Note that
applying this value means no unnumbered entries will be retrieved by PACER.
"""
if sample_size > 0:
items = items.order_by('?')[:sample_size]
q = options['queue']
throttle = CeleryThrottle(queue_name=q)
session = PacerSession(username=PACER_USERNAME, password=PACER_PASSWORD)
session.login()
for i, row in enumerate(items):
if i < options['offset']:
continue
if i >= options['limit'] > 0:
break
if i % 5000 == 0:
# Re-authenticate just in case the auto-login mechanism isn't
# working.
session = PacerSession(username=PACER_USERNAME,
password=PACER_PASSWORD)
session.login()
# All tests pass. Get the docket.
logger.info("Doing row %s: %s", i, row)
throttle.maybe_wait()
params = make_fjc_idb_lookup_params(row)
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=row.docket_number,
court_id=row.district_id,
cookies=session.cookies,
**params
).set(queue=q),
filter_docket_by_tags.s(tags, row.district_id).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id=row.district_id,
cookies=session.cookies,
tag_names=tags,
**{
'show_parties_and_counsel': True,
'show_terminated_parties': True,
'show_list_of_member_cases': False,
'doc_num_end': doc_num_end,
}
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def get_dockets(options, items, tags, sample_size=0):
"""Download dockets from PACER.
:param options: Options provided by argparse
:param items: Items from our FJC IDB database
:param tags: A list of tag names to associate with the purchased content.
:param sample_size: The number of items to get. If 0, get them all. Else,
get only this many and do it randomly.
"""
if sample_size > 0:
items = items.order_by('?')[:sample_size]
q = options['queue']
throttle = CeleryThrottle(queue_name=q)
session = PacerSession(username=PACER_USERNAME, password=PACER_PASSWORD)
session.login()
for i, row in enumerate(items):
if i < options['offset']:
continue
if i >= options['limit'] > 0:
break
if i % 5000 == 0:
# Re-authenticate just in case the auto-login mechanism isn't
# working.
session = PacerSession(username=PACER_USERNAME,
password=PACER_PASSWORD)
session.login()
# All tests pass. Get the docket.
logger.info("Doing row %s: %s", i, row)
throttle.maybe_wait()
params = make_fjc_idb_lookup_params(row)
chain(
get_pacer_case_id_and_title.s(
docket_number=row.docket_number,
court_id=row.district_id,
cookies=session.cookies,
**params
).set(queue=q),
filter_docket_by_tags.s(tags, row.district_id).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id=row.district_id,
cookies=session.cookies,
tag_names=tags,
**{
'show_parties_and_counsel': True,
'show_terminated_parties': True,
'show_list_of_member_cases': True
}
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def download_dockets(options):
"""Download dockets listed in the spreadsheet."""
with open(options["input_file"], "r") as f:
dialect = csv.Sniffer().sniff(f.read(1024))
f.seek(0)
reader = csv.DictReader(f, dialect=dialect)
q = options["queue"]
task = options["task"]
throttle = CeleryThrottle(queue_name=q,
min_items=options["queue_length"])
session = PacerSession(username=PACER_USERNAME,
password=PACER_PASSWORD)
session.login()
for i, row in enumerate(reader):
if i < options["offset"]:
continue
if i >= options["limit"] > 0:
break
throttle.maybe_wait()
logger.info("Doing row %s: %s", i, row)
if row["idb_docket_number"]:
if task == "download_student_dockets":
continue
# Zero-pad the docket number up to seven digits because Excel
# ate the leading zeros that these would normally have.
docket_number = row["idb_docket_number"].rjust(7, "0")
elif row["student_docket_number"]:
# Use the values collected by student
# researchers, then cleaned up my mlr.
docket_number = row["student_docket_number"]
else:
# No docket number; move on.
continue
court = Court.objects.get(
fjc_court_id=row["AO ID"].rjust(2, "0"),
jurisdiction=Court.FEDERAL_DISTRICT,
)
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=docket_number,
court_id=court.pk,
cookies=session.cookies,
case_name=row["Case Name"],
).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id=court.pk,
cookies=session.cookies,
tag_names=[TAG_NAME],
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def download_dockets(options):
"""Download dockets listed in the spreadsheet."""
with open(options['input_file'], 'r') as f:
dialect = csv.Sniffer().sniff(f.read(1024))
f.seek(0)
reader = csv.DictReader(f, dialect=dialect)
q = options['queue']
task = options['task']
throttle = CeleryThrottle(queue_name=q,
min_items=options['queue_length'])
session = PacerSession(username=PACER_USERNAME,
password=PACER_PASSWORD)
session.login()
for i, row in enumerate(reader):
if i < options['offset']:
continue
if i >= options['limit'] > 0:
break
throttle.maybe_wait()
logger.info("Doing row %s: %s", i, row)
if row['idb_docket_number']:
if task == 'download_student_dockets':
continue
# Zero-pad the docket number up to seven digits because Excel
# ate the leading zeros that these would normally have.
docket_number = row['idb_docket_number'].rjust(7, '0')
elif row['student_docket_number']:
# Use the values collected by student
# researchers, then cleaned up my mlr.
docket_number = row['student_docket_number']
else:
# No docket number; move on.
continue
court = Court.objects.get(fjc_court_id=row['AO ID'].rjust(2, '0'),
jurisdiction=Court.FEDERAL_DISTRICT)
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=docket_number,
court_id=court.pk,
cookies=session.cookies,
case_name=row['Case Name'],
).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id=court.pk,
cookies=session.cookies,
tag_names=[TAG_NAME],
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def get_dockets(options):
"""Download a sample of dockets from PACER matching the 7xx series of NOS
codes.
"""
nos_codes = [
LABOR_LITIGATION_OTHER, LABOR_MANAGEMENT_RELATIONS_ACT,
LABOR_MANAGEMENT_REPORT_DISCLOSURE, FAIR_LABOR_STANDARDS_ACT_CV,
RAILWAY_LABOR_ACT, FAMILY_AND_MEDICAL_LEAVE_ACT,
EMPLOYEE_RETIREMENT_INCOME_SECURITY_ACT
]
sample_size = 300
items = FjcIntegratedDatabase.objects.filter(
nature_of_suit__in=nos_codes,
date_terminated__gt='2009-01-01',
date_terminated__lt='2018-10-15',
date_filed__gt='2009-01-01').order_by('?')[:sample_size]
q = options['queue']
throttle = CeleryThrottle(queue_name=q)
session = PacerSession(username=PACER_USERNAME, password=PACER_PASSWORD)
session.login()
for i, row in enumerate(items):
if i < options['offset']:
continue
if i >= options['limit'] > 0:
break
# All tests pass. Get the docket.
logger.info("Doing row %s: %s", i, row)
logger.info("This case is from year: %s", row.date_filed.year)
throttle.maybe_wait()
case_name = '%s v. %s' % (row.plaintiff, row.defendant)
chain(
get_pacer_case_id_and_title.s(
docket_number=row.docket_number,
court_id=row.district_id,
cookies=session.cookies,
case_name=case_name,
).set(queue=q),
get_docket_by_pacer_case_id.s(court_id=row.district_id,
cookies=session.cookies,
tag_names=[TAG],
**{
'show_parties_and_counsel': True,
'show_terminated_parties': True,
'show_list_of_member_cases': True
}).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def get_dockets(options):
"""Download the dockets described in the CSV
"""
f = options["file"]
reader = csv.DictReader(f)
q = options["queue"]
throttle = CeleryThrottle(queue_name=q)
pacer_session = PacerSession(
username=PACER_USERNAME, password=PACER_PASSWORD
)
pacer_session.login()
for i, row in enumerate(reader):
if i < options["offset"]:
continue
if i >= options["limit"] > 0:
break
if i % 1000 == 0:
pacer_session = PacerSession(
username=PACER_USERNAME, password=PACER_PASSWORD
)
pacer_session.login()
logger.info("Sent %s tasks to celery so far." % i)
logger.info("Doing row %s", i)
throttle.maybe_wait()
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=make_docket_number(row["filecy"], row["docket"]),
court_id="ilnb",
cookies=pacer_session.cookies,
office_number=row["office"],
docket_number_letters="bk",
).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id="ilnb",
cookies=pacer_session.cookies,
tag_names=[TAG],
**{
"show_parties_and_counsel": True,
"show_terminated_parties": True,
"show_list_of_member_cases": True,
}
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def get_dockets(options):
"""Download the dockets described in the CSV
"""
f = options['file']
reader = csv.DictReader(f)
q = options['queue']
throttle = CeleryThrottle(queue_name=q)
pacer_session = PacerSession(username=PACER_USERNAME, password=PACER_PASSWORD)
pacer_session.login()
for i, row in enumerate(reader):
if i < options['offset']:
continue
if i >= options['limit'] > 0:
break
if i % 1000 == 0:
pacer_session = PacerSession(username=PACER_USERNAME,
password=PACER_PASSWORD)
pacer_session.login()
logger.info("Sent %s tasks to celery so far." % i)
logger.info("Doing row %s", i)
throttle.maybe_wait()
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=make_docket_number(row['filecy'], row['docket']),
court_id='ilnb',
cookies=pacer_session.cookies,
office_number=row['office'],
docket_number_letters='bk',
).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id='ilnb',
cookies=pacer_session.cookies,
tag_names=[TAG],
**{
'show_parties_and_counsel': True,
'show_terminated_parties': True,
'show_list_of_member_cases': True
}
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def update_any_missing_pacer_case_ids(options):
"""The network requests were making things far too slow and had to be
disabled during the first pass. With this method, we update any items
that are missing their pacer case ID value.
"""
ds = Docket.objects.filter(
idb_data__isnull=False,
pacer_case_id=None,
)
q = options['queue']
throttle = CeleryThrottle(queue_name=q)
session = PacerSession(username=PACER_USERNAME,
password=PACER_PASSWORD)
session.login()
for i, d in enumerate(queryset_generator(ds)):
if i < options['offset']:
continue
if i >= options['limit'] > 0:
break
if i % 5000 == 0:
# Re-authenticate just in case the auto-login mechanism isn't
# working.
session = PacerSession(username=PACER_USERNAME,
password=PACER_PASSWORD)
session.login()
throttle.maybe_wait()
logger.info("Getting pacer_case_id for item %s", d)
params = make_fjc_idb_lookup_params(d.idb_data)
chain(
get_pacer_case_id_and_title.s(
pass_through=d.pk,
docket_number=d.idb_data.docket_number,
court_id=d.idb_data.district_id,
cookies=session.cookies,
**params
).set(queue=q),
update_docket_from_hidden_api.s().set(queue=q),
).apply_async()
def download_dockets(options):
"""Download dockets listed in the spreadsheet."""
f = open(options["input_file"], "r")
dialect = csv.Sniffer().sniff(f.read(2048))
f.seek(0)
reader = csv.DictReader(f, dialect=dialect)
q = options["queue"]
throttle = CeleryThrottle(queue_name=q)
session = PacerSession(username=PACER_USERNAME, password=PACER_PASSWORD)
session.login()
for i, row in enumerate(reader):
if i < options["offset"]:
continue
if i >= options["limit"] > 0:
break
throttle.maybe_wait()
logger.info("Doing row %s: %s", i, row)
row_tag = f"{PROJECT_TAG_NAME}-{row['id']}"
if not row["district_ct"]:
chain(
get_appellate_docket_by_docket_number.s(
docket_number=row["docket_no1"],
court_id=row["cl_court"],
cookies=session.cookies,
tag_names=[PROJECT_TAG_NAME, row_tag],
# Do not get the docket entries for now. We're only
# interested in the date terminated. If it's an open case,
# we'll handle that later.
**{
"show_docket_entries": False,
"show_orig_docket": False,
"show_prior_cases": False,
"show_associated_cases": False,
"show_panel_info": True,
"show_party_atty_info": True,
"show_caption": True,
},
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
else:
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=row["docket_no1"],
court_id=row["cl_court"],
cookies=session.cookies,
case_name=row["name"],
).set(queue=q),
do_case_query_by_pacer_case_id.s(
court_id=row["cl_court"],
cookies=session.cookies,
tag_names=[PROJECT_TAG_NAME, row_tag],
).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id=row["cl_court"],
cookies=session.cookies,
tag_names=[PROJECT_TAG_NAME, row_tag],
**{
# No docket entries
"doc_num_start": 10000,
"doc_num_end": 10000,
"show_parties_and_counsel": True,
"show_terminated_parties": True,
"show_list_of_member_cases": True,
},
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
f.close()
def get_dockets(options):
"""Download the dockets described in the CSV according to the `tasks`
option.
"""
f = options['file']
reader = csv.DictReader(f)
q = options['queue']
task = options['task']
throttle = CeleryThrottle(queue_name=q)
session = PacerSession(username=PACER_USERNAME, password=PACER_PASSWORD)
session.login()
for i, row in enumerate(reader):
if i < options['offset']:
continue
if i >= options['limit'] > 0:
break
if row['Too Old'] == 'Yes':
continue
if row['Appellate/District'].lower() != task:
# Only do appellate when appellate, and district when district.
continue
# All tests pass. Get the docket.
logger.info("Doing row %s: %s", i, row)
throttle.maybe_wait()
if task == 'appellate':
chain(
get_appellate_docket_by_docket_number.s(
docket_number=row['Cleaned case_No'],
court_id=row['fjc_court_id'],
cookies=session.cookies,
tag_names=[TAG],
**{
'show_docket_entries': True,
'show_orig_docket': True,
'show_prior_cases': True,
'show_associated_cases': True,
'show_panel_info': True,
'show_party_atty_info': True,
'show_caption': True,
}).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
elif task == 'district':
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=row['Cleaned case_No'],
court_id=row['fjc_court_id'],
cookies=session.cookies,
case_name=row['Title'],
).set(queue=q),
get_docket_by_pacer_case_id.s(court_id=row['fjc_court_id'],
cookies=session.cookies,
tag_names=[TAG],
**{
'show_parties_and_counsel':
True,
'show_terminated_parties':
True,
'show_list_of_member_cases':
True
}).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def download_dockets(options):
"""Download dockets listed in the spreadsheet."""
f = open(options['input_file'], 'r')
dialect = csv.Sniffer().sniff(f.read(2048))
f.seek(0)
reader = csv.DictReader(f, dialect=dialect)
q = options['queue']
throttle = CeleryThrottle(queue_name=q,
min_items=options['queue_length'])
session = PacerSession(username=PACER_USERNAME,
password=PACER_PASSWORD)
session.login()
for i, row in enumerate(reader):
if i < options['offset']:
continue
if i >= options['limit'] > 0:
break
throttle.maybe_wait()
logger.info("Doing row %s: %s", i, row)
row_tag = '%s-%s' % (PROJECT_TAG_NAME, row['id'])
if not row['district_ct']:
chain(
get_appellate_docket_by_docket_number.s(
docket_number=row['docket_no1'],
court_id=row['cl_court'],
cookies=session.cookies,
tag_names=[PROJECT_TAG_NAME, row_tag],
# Do not get the docket entries for now. We're only
# interested in the date terminated. If it's an open case,
# we'll handle that later.
**{
'show_docket_entries': False,
'show_orig_docket': False,
'show_prior_cases': False,
'show_associated_cases': False,
'show_panel_info': True,
'show_party_atty_info': True,
'show_caption': True,
}
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
else:
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=row['docket_no1'],
court_id=row['cl_court'],
cookies=session.cookies,
case_name=row['name'],
).set(queue=q),
do_case_query_by_pacer_case_id.s(
court_id=row['cl_court'],
cookies=session.cookies,
tag_names=[PROJECT_TAG_NAME, row_tag],
).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id=row['cl_court'],
cookies=session.cookies,
tag_names=[PROJECT_TAG_NAME, row_tag],
**{
# No docket entries
'doc_num_start': 10000,
'doc_num_end': 10000,
'show_parties_and_counsel': True,
'show_terminated_parties': True,
'show_list_of_member_cases': True,
}
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
f.close()
def get_dockets(options):
"""Download the dockets described in the CSV according to the `tasks`
option.
"""
f = options['file']
reader = csv.DictReader(f)
q = options['queue']
task = options['task']
throttle = CeleryThrottle(queue_name=q)
session = PacerSession(username=PACER_USERNAME, password=PACER_PASSWORD)
session.login()
for i, row in enumerate(reader):
if i < options['offset']:
continue
if i >= options['limit'] > 0:
break
if row['Too Old'] == 'Yes':
continue
if row['Appellate/District'].lower() != task:
# Only do appellate when appellate, and district when district.
continue
# All tests pass. Get the docket.
logger.info("Doing row %s: %s", i, row)
throttle.maybe_wait()
if task == 'appellate':
chain(
get_appellate_docket_by_docket_number.s(
docket_number=row['Cleaned case_No'],
court_id=row['fjc_court_id'],
cookies=session.cookies,
tag_names=[TAG],
**{
'show_docket_entries': True,
'show_orig_docket': True,
'show_prior_cases': True,
'show_associated_cases': True,
'show_panel_info': True,
'show_party_atty_info': True,
'show_caption': True,
}
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
elif task == 'district':
chain(
get_pacer_case_id_and_title.s(
pass_through=None,
docket_number=row['Cleaned case_No'],
court_id=row['fjc_court_id'],
cookies=session.cookies,
case_name=row['Title'],
).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id=row['fjc_court_id'],
cookies=session.cookies,
tag_names=[TAG],
**{
'show_parties_and_counsel': True,
'show_terminated_parties': True,
'show_list_of_member_cases': True
}
).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
def download_dockets(options):
"""Download dockets listed in the spreadsheet."""
f = open(options['input_file'], 'r')
dialect = csv.Sniffer().sniff(f.read(2048))
f.seek(0)
reader = csv.DictReader(f, dialect=dialect)
q = options['queue']
throttle = CeleryThrottle(queue_name=q, min_items=options['queue_length'])
session = PacerSession(username=PACER_USERNAME, password=PACER_PASSWORD)
session.login()
for i, row in enumerate(reader):
if i < options['offset']:
continue
if i >= options['limit'] > 0:
break
throttle.maybe_wait()
logger.info("Doing row %s: %s", i, row)
row_tag = '%s-%s' % (PROJECT_TAG_NAME, row['id'])
if not row['district_ct']:
chain(
get_appellate_docket_by_docket_number.s(
docket_number=row['docket_no1'],
court_id=row['cl_court'],
cookies=session.cookies,
tag_names=[PROJECT_TAG_NAME, row_tag],
# Do not get the docket entries for now. We're only
# interested in the date terminated. If it's an open case,
# we'll handle that later.
**{
'show_docket_entries': False,
'show_orig_docket': False,
'show_prior_cases': False,
'show_associated_cases': False,
'show_panel_info': True,
'show_party_atty_info': True,
'show_caption': True,
}).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
else:
chain(
get_pacer_case_id_and_title.s(
docket_number=row['docket_no1'],
court_id=row['cl_court'],
cookies=session.cookies,
case_name=row['name'],
).set(queue=q),
do_case_query_by_pacer_case_id.s(
court_id=row['cl_court'],
cookies=session.cookies,
tag_names=[PROJECT_TAG_NAME, row_tag],
).set(queue=q),
get_docket_by_pacer_case_id.s(
court_id=row['cl_court'],
cookies=session.cookies,
tag_names=[PROJECT_TAG_NAME, row_tag],
**{
# No docket entries
'doc_num_start': 10000,
'doc_num_end': 10000,
'show_parties_and_counsel': True,
'show_terminated_parties': True,
'show_list_of_member_cases': True,
}).set(queue=q),
add_or_update_recap_docket.s().set(queue=q),
).apply_async()
f.close()
