def make_mag_bk_judge(item, testing=False):
"""Takes the federal judge data <item> and associates it with a Judge
object. Returns a Judge object. To be used for importing bankruptcy
and magistrate judges. Example data below:
CL_ID: fjc-bk-12
COURT: AR,E
JUDGE_NAME: JONES, PHYLLIS M.
POSITION: Bankruptcy
NAME_LAST: Jones
NAME_FIRST: Phyllis
NAME_MIDDLE: M.
NAME_SUFFIX:
GENDER: Female
START_DATE: 2015-01-07
START_DATE_GRANULARITY: %Y-%m-%d
SOURCE: https://www.uscourts.gov/judicial-milestones/phyllis-m-jones
"""
name = "{CL_ID}: {NAME_FIRST} {NAME_LAST}".format(**item)
print("Now processing: %s" % name)
if Person.objects.filter(cl_id=item["CL_ID"]).exists():
raise ValidationError(
"CL_ID already exists for the record being imported: %s" % name)
if Person.objects.filter(
name_first=item["NAME_FIRST"],
name_middle=item["NAME_MIDDLE"],
name_last=item["NAME_LAST"],
).exists():
raise ValidationError(
"Name already exists for record being imported %s" % name)
if not pd.isnull(item["NAME_MIDDLE"]):
if len(item["NAME_MIDDLE"]) == 1:
item["NAME_MIDDLE"] += "."
if not pd.isnull(item["GENDER"]):
gender = get_gender(item["GENDER"])
if not item["NAME_SUFFIX"] == "":
suffix = get_suffix(item["NAME_SUFFIX"])
else:
suffix = ""
# Instantiate Judge object.
person = Person(
name_first=item["NAME_FIRST"],
name_middle=item["NAME_MIDDLE"],
name_last=item["NAME_LAST"],
name_suffix=suffix,
gender=gender,
cl_id=item["CL_ID"],
)
if not testing:
person.save()
# Add position.
if re.search("Bankruptcy", item["POSITION"]):
position_type = Position.JUDGE
if item["COURT"]:
court = FJC_BANKRUPTCY_COURTS[item["COURT"]]
else:
position_type = Position.MAGISTRATE
if item["COURT"]:
court = FJC_DISTRICT_COURTS[item["COURT"]]
date_start = process_date_string(item["START_DATE"])
position = Position(
person=person,
position_type=position_type,
court_id=court,
date_start=date_start,
date_granularity_start=item["START_DATE_GRANULARITY"],
)
if not testing:
position.save()
sources = Source(person=person,
url=item["SOURCE"],
date_accessed=str(date.today()))
if not testing:
sources.save()
def make_federal_judge(item, testing=False):
"""Takes the federal judge data <item> and associates it with a Judge object.
Returns a Judge object.
"""
date_dob, date_granularity_dob = process_date(item["Birth Year"],
item["Birth Month"],
item["Birth Day"])
dob_city = item["Birth City"]
dob_state = item["Birth State"]
# if foreign-born, leave blank for now.
if len(dob_state) > 2:
dob_state = ""
name = "%s: %s %s %s" % (
item["cl_id"],
item["First Name"],
item["Last Name"],
str(date_dob),
)
fjc_check = Person.objects.filter(fjc_id=item["jid"])
if len(fjc_check) > 0:
print("Warning: %s exists" % name)
return
pres_check = Person.objects.filter(
name_first=item["First Name"],
name_last=item["Last Name"],
date_dob=date_dob,
)
if not testing:
print("Now processing: %s" % name)
if len(pres_check) > 0:
print("%s is a president." % name)
person = pres_check[0]
person.fjc_id = item["jid"]
else:
date_dod, date_granularity_dod = process_date(item["Death Year"],
item["Death Month"],
item["Death Day"])
dod_city = item["Death City"]
dod_state = item["Death State"]
# if foreign-dead, leave blank for now.
if len(dod_state) > 2:
dod_state = ""
if not pd.isnull(item["Middle Name"]):
if len(item["Middle Name"]) == 1:
item["Middle Name"] += "."
if not pd.isnull(item["Gender"]):
gender = get_gender(item["Gender"])
# Instantiate Judge object.
person = Person(
name_first=item["First Name"],
name_middle=item["Middle Name"],
name_last=item["Last Name"],
name_suffix=get_suffix(item["Suffix"]),
gender=gender,
fjc_id=item["jid"],
cl_id=item["cl_id"],
date_dob=date_dob,
date_granularity_dob=date_granularity_dob,
dob_city=dob_city,
dob_state=dob_state,
date_dod=date_dod,
date_granularity_dod=date_granularity_dod,
dod_city=dod_city,
dod_state=dod_state,
)
if not testing:
person.save()
listraces = get_races(item["Race or Ethnicity"])
races = [Race.objects.get(race=r) for r in listraces]
for r in races:
if not testing:
person.race.add(r)
add_positions_from_row(item, person, testing)
# add education items (up to 5 of them)
for schoolnum in range(1, 6):
school_str = " (%s)" % schoolnum
schoolname = item["School" + school_str]
if pd.isnull(schoolname):
continue
if pd.isnull(item["Degree" + school_str]):
degs = [""]
else:
degs = [x.strip() for x in item["Degree" + school_str].split(";")]
for degtype in degs:
deg_level = get_degree_level(degtype)
degyear = item["Degree Year" + school_str]
try:
int(degyear)
except:
degyear = None
school = get_school(schoolname)
if school is not None:
degree = Education(
person=person,
school=school,
degree_detail=degtype,
degree_level=deg_level,
degree_year=degyear,
)
if not testing:
degree.save()
# Non-judicial positions
titles, locations, startyears, endyears = transform_employ(
item["Professional Career"])
# There is no field in the FJC data with variables commented out below
# (i.e. "Bankruptcy and Magistrate service"). This is commented out
# for reference as pulling out bankruptcy and magistrate service does
# need to be implemented (yet).
# titles2, locations2, startyears2, endyears2 = transform_bankruptcy(item['Bankruptcy and Magistrate service'])
# titles = titles + titles2
# locations = locations + locations2
# startyears = startyears + startyears2
# endyears = endyears + endyears2
for i in range(len(titles)):
job_title = titles[i]
if (pd.isnull(job_title) or job_title == ""
or job_title.startswith("Nominated")):
continue
location = locations[i]
start_year = startyears[i]
end_year = endyears[i]
job_title = job_title.strip()
if pd.isnull(start_year) or start_year == "":
date_start = None
date_start_granularity = ""
else:
try:
start_year = int(start_year)
except:
continue
date_start = date(start_year, 1, 1)
date_start_granularity = GRANULARITY_YEAR
if not pd.isnull(end_year) and end_year.isdigit():
end_year = int(end_year)
date_end = date(end_year, 1, 1)
date_end_granularity = GRANULARITY_YEAR
else:
date_end = None
date_end_granularity = ""
if not pd.isnull(location):
location = location.strip()
if "," in location:
city, state = [x.strip() for x in location.split(",")]
org = ""
if state in STATES_NORMALIZED.values():
pass
elif state.lower() in STATES_NORMALIZED.keys():
state = STATES_NORMALIZED[state.lower()]
else:
city, state = "", ""
org = location
else:
city, state = "", ""
org = location
# test for schools and courts
else:
city, state, org = "", "", ""
position = Position(
person=person,
job_title=job_title,
date_start=date_start,
date_granularity_start=date_start_granularity,
date_termination=date_end,
date_granularity_termination=date_end_granularity,
location_city=city,
location_state=state,
organization_name=org,
)
if not testing:
try:
position.save()
except Exception:
continue
