def test_historic_badges(self):
officer = OfficerFactory()
expect(officer.historic_badges).to.be.empty()
OfficerBadgeNumberFactory(officer=officer, star='000', current=True)
OfficerBadgeNumberFactory(officer=officer, star='123', current=False)
OfficerBadgeNumberFactory(officer=officer, star='456', current=False)
expect(list(officer.historic_badges)).to.eq(['123', '456'])
def test_current_badge(self):
officer_1 = OfficerFactory()
officer_2 = OfficerFactory()
OfficerBadgeNumberFactory(officer=officer_1, star='123', current=True)
OfficerBadgeNumberFactory(officer=officer_2, current=False)
officer_cache_manager.build_cached_columns()
officer_1.refresh_from_db()
expect(officer_1.current_badge).to.eq('123')
expect(officer_2.current_badge).to.eq(None)
def test_upload_cr_attachment_request_to_foia_with_cpd_after_2006_has_badge_number(
self, airtable_mock):
airtable_mock.insert.return_value = {'id': 'some_airtable_record_id'}
allegation = AllegationFactory(crid='123456',
incident_date=datetime(2007,
12,
31,
tzinfo=pytz.utc))
attachment_request = AttachmentRequestFactory(
allegation=allegation, email='*****@*****.**')
officer_1 = OfficerFactory(id=1, first_name='Marry', last_name='Jane')
officer_2 = OfficerFactory(id=2, first_name='John', last_name='Henry')
OfficerBadgeNumberFactory(officer=officer_1, star='12345')
OfficerBadgeNumberFactory(officer=officer_2, star='56789')
investigator_1 = InvestigatorFactory(officer=officer_1)
investigator_2 = InvestigatorFactory(officer=officer_1)
InvestigatorAllegationFactory(allegation=allegation,
current_star=None,
investigator=investigator_1)
InvestigatorAllegationFactory(allegation=allegation,
current_star=None,
investigator=investigator_2)
OfficerAllegationFactory(allegation=allegation, officer=officer_1)
OfficerAllegationFactory(allegation=allegation, officer=officer_2)
expected_airtable_data = {
'Explanation':
'Officers: John Henry(ID 2), Marry Jane(ID 1)',
'Project': ['CPDP'],
'Agency': ['CPD_AGENCY_ID'],
'Requested For':
'CR 123456',
'Requestor': [{
'id': 'usrGiZFcyZ6wHTYWd',
'email': '*****@*****.**',
'name': 'Rajiv Sinclair'
}],
'Date requested by user':
attachment_request.created_at.strftime('%Y-%m-%d'),
'Requester Email':
'*****@*****.**'
}
CRRequestAirTableUploader.upload()
attachment_request.refresh_from_db()
airtable_mock.insert.assert_called_with(expected_airtable_data)
expect(
attachment_request.airtable_id).to.be.eq('some_airtable_record_id')
def test_get_involvements(self, police_witness_serializer_mock, investigator_allegation_serializer_mock):
allegation = AllegationFactory()
officer_1 = OfficerFactory()
officer_2 = OfficerFactory()
PoliceWitnessFactory(officer=officer_1, allegation=allegation)
PoliceWitnessFactory(officer=officer_2, allegation=allegation)
OfficerBadgeNumberFactory(officer=officer_1, star='456789')
investigator_1 = InvestigatorFactory(officer=officer_1)
investigator_2 = InvestigatorFactory(officer=officer_2)
investigator_3 = InvestigatorFactory()
investigator_allegation_1 = InvestigatorAllegationFactory(allegation=allegation, investigator=investigator_1)
investigator_allegation_2 = InvestigatorAllegationFactory(allegation=allegation, investigator=investigator_2)
investigator_allegation_3 = InvestigatorAllegationFactory(allegation=allegation, investigator=investigator_3)
investigator_allegations = [investigator_allegation_1, investigator_allegation_2, investigator_allegation_3]
expected_has_badge_numbers = {
investigator_allegation_1.id: True,
investigator_allegation_2.id: False,
investigator_allegation_3.id: False,
}
result = CRSerializer(allegation).data
investigator_allegations_arg = investigator_allegation_serializer_mock.call_args[0][0]
police_witnesses_arg = police_witness_serializer_mock.call_args[0][0]
expect(set(investigator_allegations_arg)).to.eq(set(investigator_allegations))
for obj in investigator_allegations_arg:
expect(obj.has_badge_number).to.eq(expected_has_badge_numbers[obj.id])
expect(set(police_witnesses_arg)).to.eq(set([officer_1, officer_2]))
expect(result['involvements']).to.eq(
[{'officer_id': 1}, {'officer_id': 2}, {'officer_id': 4}, {'officer_id': 3}]
)
def test_extract_info(self):
officer = OfficerFactory(id=123,
first_name='Alex',
last_name='Mack',
rank='5',
race='White',
gender='M',
appointed_date=date(2017, 2, 27),
resignation_date=date(2017, 12, 27),
active='Yes',
birth_year=1910,
complaint_percentile=99.8,
honorable_mention_percentile=98,
tags=['Jason VanDyke'])
TRRFactory(officer=officer,
trr_datetime=datetime(2002, 9, 29, tzinfo=pytz.utc))
SalaryFactory(officer=officer, salary=9000)
AwardFactory(officer=officer, award_type='Honorable Mention')
AwardFactory(officer=officer, award_type='Complimentary Letter')
AwardFactory(officer=officer, award_type='Honored Police Star')
AwardFactory(officer=officer, award_type='Lambert Tree')
OfficerHistoryFactory(officer=officer,
unit__id=1001,
unit__unit_name='001',
unit__description='Hyde Park D',
effective_date=date(2010, 1, 1),
end_date=date(2011, 1, 1))
OfficerHistoryFactory(officer=officer,
unit__id=1002,
unit__unit_name='002',
unit__description='Tactical',
effective_date=date(2011, 1, 2))
OfficerBadgeNumberFactory(officer=officer, star='123456', current=True)
OfficerBadgeNumberFactory(officer=officer, star='123', current=False)
OfficerBadgeNumberFactory(officer=officer, star='456', current=False)
allegation = AllegationFactory(
incident_date=datetime(2000, 4, 26, tzinfo=pytz.utc))
OfficerAllegationFactory(
officer=officer,
final_finding='SU',
start_date=date(2000, 1, 1),
allegation_category__category='Illegal Search',
allegation=allegation,
disciplined=True)
ComplainantFactory(allegation=allegation,
race='White',
age=18,
gender='M')
rows = self.extract_data()
expect(rows).to.have.length(1)
expect(rows[0]).to.eq({
'id':
123,
'full_name':
'Alex Mack',
'unit': {
'id': 1002,
'unit_name': '002',
'description': 'Tactical',
'long_unit_name': 'Unit 002'
},
'rank':
'5',
'race':
'White',
'badge':
'123456',
'badge_keyword':
'123456',
'historic_badges': ['123', '456'],
'historic_badges_keyword': ['123', '456'],
'historic_units': [{
'id': 1002,
'unit_name': '002',
'description': 'Tactical',
'long_unit_name': 'Unit 002',
}, {
'id': 1001,
'unit_name': '001',
'description': 'Hyde Park D',
'long_unit_name': 'Unit 001',
}],
'gender':
'Male',
'date_of_appt':
'2017-02-27',
'date_of_resignation':
'2017-12-27',
'active':
'Active',
'birth_year':
1910,
'complaint_records': {
'count':
1,
'sustained_count':
1,
'items': [{
'year': 2000,
'count': 1,
'sustained_count': 1
}],
'facets': [{
'name':
'category',
'entries': [{
'name':
'Illegal Search',
'count':
1,
'sustained_count':
1,
'items': [{
'year': 2000,
'name': 'Illegal Search',
'count': 1,
'sustained_count': 1
}]
}]
}, {
'name':
'complainant race',
'entries': [{
'name':
'White',
'count':
1,
'sustained_count':
1,
'items': [{
'year': 2000,
'name': 'White',
'count': 1,
'sustained_count': 1
}]
}]
}, {
'name':
'complainant age',
'entries': [{
'name':
'<20',
'count':
1,
'sustained_count':
1,
'items': [{
'year': 2000,
'name': '<20',
'count': 1,
'sustained_count': 1
}]
}]
}, {
'name':
'complainant gender',
'entries': [{
'name':
'Male',
'count':
1,
'sustained_count':
1,
'items': [{
'year': 2000,
'name': 'Male',
'count': 1,
'sustained_count': 1
}]
}]
}]
},
'allegation_count':
1,
'complaint_percentile':
Decimal('99.8'),
'honorable_mention_count':
1,
'honorable_mention_percentile':
98,
'has_visual_token':
False,
'sustained_count':
1,
'discipline_count':
1,
'civilian_compliment_count':
1,
'trr_count':
1,
'major_award_count':
2,
'tags': ['Jason VanDyke'],
'to':
'/officer/123/alex-mack/',
'url':
'http://test.com/officer/alex-mack/123',
'current_salary':
9000,
'unsustained_count':
0,
'coaccusals': [],
'current_allegation_percentile':
None,
'percentiles': [],
'cr_incident_dates': ['2000-04-26'],
'trr_datetimes': ['2002-09-29'],
'internal_allegation_percentile':
None,
'trr_percentile':
None,
'civilian_allegation_percentile':
None,
})
def test_summary(self):
officer = OfficerFactory(
tags=[],
first_name='Kevin', last_name='Kerl', id=123, race='White', gender='M',
appointed_date=date(2017, 2, 27), rank='PO', resignation_date=date(2017, 12, 27),
active=ACTIVE_YES_CHOICE, birth_year=1910, complaint_percentile=32.5,
sustained_count=1, allegation_count=1, discipline_count=1, trr_count=1,
civilian_compliment_count=1, honorable_mention_count=1, major_award_count=1,
last_unit_id=1, current_badge='123456', current_salary=90000, has_unique_name=True
)
allegation = AllegationFactory()
allegation_category = AllegationCategoryFactory(category='Use of Force')
OfficerHistoryFactory(officer=officer, unit=PoliceUnitFactory(id=1, unit_name='CAND', description=''))
ComplainantFactory(allegation=allegation, race='White', age=18, gender='F')
OfficerBadgeNumberFactory(officer=officer, star='123456', current=True)
OfficerAllegationFactory(
officer=officer, allegation=allegation, allegation_category=allegation_category,
final_finding='SU', start_date=date(2000, 1, 1), disciplined=True
)
AwardFactory(officer=officer, award_type='Complimentary Letter')
AwardFactory(officer=officer, award_type='Honored Police Star')
AwardFactory(officer=officer, award_type='Honorable Mention')
SalaryFactory(officer=officer, salary=50000, year=2015)
SalaryFactory(officer=officer, salary=90000, year=2017)
TRRFactory(officer=officer)
officer_cache_manager.build_cached_columns()
allegation_cache_manager.cache_data()
response = self.client.get(reverse('api-v2:officers-summary', kwargs={'pk': 123}))
expect(response.status_code).to.eq(status.HTTP_200_OK)
expected_data = {
'id': 123,
'unit': {
'id': 1,
'unit_name': 'CAND',
'description': '',
'long_unit_name': 'Unit CAND',
},
'date_of_appt': '2017-02-27',
'date_of_resignation': '2017-12-27',
'active': 'Active',
'rank': 'PO',
'full_name': 'Kevin Kerl',
'race': 'White',
'badge': '123456',
'historic_units': [{
'id': 1,
'unit_name': 'CAND',
'description': '',
'long_unit_name': 'Unit CAND'
}],
'gender': 'Male',
'birth_year': 1910,
'sustained_count': 1,
'civilian_compliment_count': 1,
'allegation_count': 1,
'discipline_count': 1,
'honorable_mention_count': 1,
'to': '/officer/123/kevin-kerl/',
'url': 'http://cpdb.lvh.me/officer/kevin-kerl/123',
'current_salary': 90000,
'trr_count': 1,
'major_award_count': 1,
'unsustained_count': 0,
'percentile_allegation': '32.5000',
'coaccusals': [],
'percentiles': [],
'tags': [],
'historic_badges': [],
'has_unique_name': True
}
expect(response.data).to.eq(expected_data)
def test_retrieve(self):
area = AreaFactory(name='Lincoln Square')
officer1 = OfficerFactory(
id=123,
first_name='Mr',
last_name='Foo',
gender='M',
race='White',
rank='Officer',
appointed_date=date(2001, 1, 1),
birth_year=1993,
complaint_percentile=4.4,
civilian_allegation_percentile=1.1,
internal_allegation_percentile=2.2,
trr_percentile=3.3,
allegation_count=1,
sustained_count=1,
)
OfficerBadgeNumberFactory(officer=officer1, star='12345', current=True)
allegation = AllegationFactory(crid='12345',
point=Point(12, 21),
incident_date=datetime(2002,
2,
28,
tzinfo=pytz.utc),
add1=3510,
add2='Michigan Ave',
city='Chicago',
location='Police Communications System',
beat=area,
is_officer_complaint=False,
summary='Summary',
first_start_date=date(2003, 3, 20),
first_end_date=date(2006, 5, 26))
ComplainantFactory(allegation=allegation,
gender='M',
race='Black',
age='18')
VictimFactory(allegation=allegation, gender='M', race='Black', age=53)
OfficerAllegationFactory(
officer=officer1,
allegation=allegation,
final_finding='SU',
disciplined=True,
final_outcome='Separation',
recc_outcome='10 Day Suspension',
start_date=date(2003, 3, 20),
end_date=date(2006, 5, 26),
allegation_category=AllegationCategoryFactory(
category='Operation/Personnel Violations',
allegation_name='Secondary/Special Employment'))
officer = OfficerFactory(
id=3,
first_name='Raymond',
last_name='Piwinicki',
appointed_date=date(2001, 5, 1),
complaint_percentile=4.4,
trr_percentile=5.5,
allegation_count=1,
sustained_count=1,
)
OfficerAllegationFactory(officer=officer,
final_finding='SU',
start_date=date(2003, 2, 28),
allegation__incident_date=datetime(
2002, 2, 28, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
PoliceWitnessFactory(officer=officer, allegation=allegation)
investigator = OfficerFactory(
id=1,
first_name='Ellis',
last_name='Skol',
appointed_date=date(2001, 5, 1),
complaint_percentile=6.6,
civilian_allegation_percentile=7.7,
internal_allegation_percentile=8.8,
allegation_count=1,
sustained_count=0,
)
OfficerAllegationFactory(officer=investigator,
final_finding='NS',
start_date=date(2003, 2, 28),
allegation__incident_date=datetime(
2002, 2, 28, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
investigator = InvestigatorFactory(officer=investigator)
InvestigatorAllegationFactory(allegation=allegation,
investigator=investigator,
current_rank='IPRA investigator')
AttachmentFileFactory(tag='TRR',
allegation=allegation,
title='CR document',
id='123456',
url='http://cr-document.com/',
file_type=MEDIA_TYPE_DOCUMENT)
AttachmentFileFactory(tag='TRR',
allegation=allegation,
title='CR arrest report document',
url='http://cr-document.com/',
file_type=MEDIA_TYPE_DOCUMENT)
AttachmentFileFactory(tag='AR',
allegation=allegation,
title='CR document 2',
id='654321',
url='http://AR-document.com/',
file_type=MEDIA_TYPE_DOCUMENT)
officer_cache_manager.build_cached_columns()
allegation_cache_manager.cache_data()
response = self.client.get(
reverse('api-v2:cr-detail', kwargs={'pk': '12345'}))
expect(response.status_code).to.eq(status.HTTP_200_OK)
expect(dict(response.data)).to.eq({
'crid':
'12345',
'most_common_category': {
'category': 'Operation/Personnel Violations',
'allegation_name': 'Secondary/Special Employment'
},
'coaccused': [{
'id': 123,
'full_name': 'Mr Foo',
'gender': 'Male',
'race': 'White',
'rank': 'Officer',
'birth_year': 1993,
'recommended_outcome': '10 Day Suspension',
'final_outcome': 'Separation',
'final_finding': 'Sustained',
'category': 'Operation/Personnel Violations',
'complaint_count': 1,
'sustained_count': 1,
'percentile_allegation': '4.4000',
'percentile_allegation_civilian': '1.1000',
'percentile_allegation_internal': '2.2000',
'percentile_trr': '3.3000',
'disciplined': True
}],
'complainants': [{
'race': 'Black',
'gender': 'Male',
'age': 18
}],
'victims': [{
'race': 'Black',
'gender': 'Male',
'age': 53
}],
'point': {
'lon': 12.0,
'lat': 21.0
},
'summary':
'Summary',
'incident_date':
'2002-02-28',
'start_date':
'2003-03-20',
'end_date':
'2006-05-26',
'address':
'3510 Michigan Ave, Chicago',
'location':
'Police Communications System',
'beat':
'Lincoln Square',
'involvements': [{
'involved_type': 'investigator',
'officer_id': 1,
'full_name': 'Ellis Skol',
'badge': 'CPD',
'percentile_allegation': '6.6000',
'percentile_allegation_civilian': '7.7000',
'percentile_allegation_internal': '8.8000',
}, {
'involved_type': 'police_witness',
'officer_id': 3,
'full_name': 'Raymond Piwinicki',
'allegation_count': 1,
'sustained_count': 1,
'percentile_trr': '5.5000',
'percentile_allegation': '4.4000',
}],
'attachments': [{
'title': 'CR document',
'file_type': 'document',
'url': 'http://cr-document.com/',
'id': '123456',
}]
})
def test_serialization(self):
unit = PoliceUnitFactory(
id=4,
unit_name='004',
description='District 004',
)
old_unit = PoliceUnitFactory(
id=5,
unit_name='005',
description='District 005',
)
officer = OfficerFactory(id=123,
first_name='Michael',
last_name='Flynn',
last_unit=unit,
appointed_date=date(2000, 1, 2),
resignation_date=date(2010, 2, 3),
active='Yes',
rank='Sergeant',
race='Black',
gender='F',
current_badge='456',
birth_year=1950,
current_salary=10000,
has_unique_name=False)
OfficerBadgeNumberFactory(officer=officer, current=False, star='789')
OfficerBadgeNumberFactory(officer=officer, current=True, star='456')
OfficerHistoryFactory(officer=officer,
unit=old_unit,
effective_date=date(2002, 1, 2))
OfficerHistoryFactory(officer=officer,
unit=unit,
effective_date=date(2004, 1, 2))
expect(OfficerSummarySerializer(officer).data).to.eq({
'id':
123,
'unit': {
'id': 4,
'unit_name': '004',
'description': 'District 004',
'long_unit_name': 'Unit 004',
},
'date_of_appt':
'2000-01-02',
'date_of_resignation':
'2010-02-03',
'active':
'Active',
'rank':
'Sergeant',
'full_name':
'Michael Flynn',
'race':
'Black',
'badge':
'456',
'historic_badges': ['789'],
'historic_units': [{
'id': 4,
'unit_name': '004',
'description': 'District 004',
'long_unit_name': 'Unit 004',
}, {
'id': 5,
'unit_name': '005',
'description': 'District 005',
'long_unit_name': 'Unit 005',
}],
'gender':
'Female',
'birth_year':
1950,
'current_salary':
10000,
'has_unique_name':
False,
})
def test_serialization(self, _):
unit = PoliceUnitFactory(
id=4,
unit_name='004',
description='District 004',
)
old_unit = PoliceUnitFactory(
id=5,
unit_name='005',
description='District 005',
)
officer = OfficerFactory(id=123,
tags=['tag1', 'tag2'],
first_name='Michael',
last_name='Flynn',
last_unit=unit,
appointed_date=date(2000, 1, 2),
resignation_date=date(2010, 2, 3),
active='Yes',
rank='Sergeant',
race='Black',
gender='F',
current_badge='456',
birth_year=1950,
current_salary=10000,
allegation_count=20,
complaint_percentile='99.9900',
honorable_mention_count=3,
sustained_count=4,
unsustained_count=5,
discipline_count=6,
civilian_compliment_count=2,
trr_count=7,
major_award_count=8,
honorable_mention_percentile='88.8800',
has_unique_name=True)
OfficerBadgeNumberFactory(officer=officer, current=False, star='789')
OfficerBadgeNumberFactory(officer=officer, current=True, star='456')
OfficerHistoryFactory(officer=officer,
unit=old_unit,
effective_date=date(2002, 1, 2))
OfficerHistoryFactory(officer=officer,
unit=unit,
effective_date=date(2004, 1, 2))
OfficerYearlyPercentileFactory(id=285,
officer=officer,
year=2002,
percentile_trr='99.88',
percentile_allegation=None,
percentile_allegation_civilian='77.66',
percentile_allegation_internal='66.55')
OfficerYearlyPercentileFactory(id=286,
officer=officer,
year=2003,
percentile_trr='99.99',
percentile_allegation='88.88',
percentile_allegation_civilian='77.77',
percentile_allegation_internal='66.66')
expect(OfficerInfoSerializer(officer).data).to.eq({
'id':
123,
'unit': {
'id': 4,
'unit_name': '004',
'description': 'District 004',
'long_unit_name': 'Unit 004',
},
'date_of_appt':
'2000-01-02',
'date_of_resignation':
'2010-02-03',
'active':
'Active',
'rank':
'Sergeant',
'full_name':
'Michael Flynn',
'has_unique_name':
True,
'race':
'Black',
'badge':
'456',
'historic_badges': ['789'],
'historic_units': [{
'id': 4,
'unit_name': '004',
'description': 'District 004',
'long_unit_name': 'Unit 004',
}, {
'id': 5,
'unit_name': '005',
'description': 'District 005',
'long_unit_name': 'Unit 005',
}],
'gender':
'Female',
'birth_year':
1950,
'current_salary':
10000,
'allegation_count':
20,
'percentile_allegation':
'99.9900',
'honorable_mention_count':
3,
'sustained_count':
4,
'unsustained_count':
5,
'discipline_count':
6,
'civilian_compliment_count':
2,
'trr_count':
7,
'major_award_count':
8,
'honorable_mention_percentile':
'88.8800',
'to':
'/officer/123/michael-flynn/',
'url':
f'{settings.V1_URL}/officer/michael-flynn/123',
'tags': ['tag1', 'tag2'],
'coaccusals': [{
'id': 789,
'coaccusal_count': 10
}],
'percentiles': [
{
'id': 285,
'year': 2002,
'percentile_trr': '99.8800',
'percentile_allegation_civilian': '77.6600',
'percentile_allegation_internal': '66.5500',
},
{
'id': 286,
'year': 2003,
'percentile_trr': '99.9900',
'percentile_allegation': '88.8800',
'percentile_allegation_civilian': '77.7700',
'percentile_allegation_internal': '66.6600',
},
]
})
def test_annotate_investigated_by_cpd(self):
allegation_1 = AllegationFactory(crid='001',
incident_date=datetime(
2005, 12, 31, tzinfo=pytz.utc))
allegation_2 = AllegationFactory(crid='002',
incident_date=datetime(
2007, 12, 31, tzinfo=pytz.utc))
allegation_3 = AllegationFactory(crid='003',
incident_date=datetime(
2007, 12, 31, tzinfo=pytz.utc))
allegation_4 = AllegationFactory(crid='004',
incident_date=datetime(
2007, 12, 31, tzinfo=pytz.utc))
allegation_5 = AllegationFactory(crid='005',
incident_date=datetime(
2007, 12, 31, tzinfo=pytz.utc))
allegation_6 = AllegationFactory(crid='006',
incident_date=datetime(
2007, 12, 31, tzinfo=pytz.utc))
attachment_request_1 = AttachmentRequestFactory(
allegation=allegation_1)
attachment_request_2 = AttachmentRequestFactory(
allegation=allegation_2)
attachment_request_3 = AttachmentRequestFactory(
allegation=allegation_3)
attachment_request_4 = AttachmentRequestFactory(
allegation=allegation_4)
attachment_request_5 = AttachmentRequestFactory(
allegation=allegation_5)
attachment_request_6 = AttachmentRequestFactory(
allegation=allegation_6)
officer_1 = OfficerFactory()
officer_2 = OfficerFactory()
OfficerBadgeNumberFactory(officer=officer_2, star='12345')
investigator_1 = InvestigatorFactory(officer=officer_1)
investigator_2 = InvestigatorFactory(officer=officer_2)
InvestigatorAllegationFactory(allegation=allegation_3,
current_star='123456')
InvestigatorAllegationFactory(allegation=allegation_5,
current_star=None,
investigator=investigator_1)
InvestigatorAllegationFactory(allegation=allegation_6,
current_star=None,
investigator=investigator_2)
expected_results = {
attachment_request_1.id: True,
attachment_request_2.id: False,
attachment_request_3.id: True,
attachment_request_4.id: False,
attachment_request_5.id: False,
attachment_request_6.id: True,
}
for attachment_request in AttachmentRequest.objects.annotate_investigated_by_cpd(
):
expect(attachment_request.investigated_by_cpd).to.be.eq(
expected_results[attachment_request.id])
def test_retrieve_data_range_too_small_cause_no_percentiles(self):
officer = OfficerFactory(
tags=[],
first_name='Kevin', last_name='Kerl', id=123, race='White', gender='M',
appointed_date=date(2002, 2, 27), rank='PO', resignation_date=date(2017, 12, 27),
active=ACTIVE_YES_CHOICE, birth_year=1960, complaint_percentile=32.5,
sustained_count=1, allegation_count=2, discipline_count=1, trr_count=1,
civilian_compliment_count=1, honorable_mention_count=1, major_award_count=1,
last_unit_id=1, current_badge='123456'
)
allegation = AllegationFactory(incident_date=datetime(2002, 3, 1, tzinfo=pytz.utc))
internal_allegation = AllegationFactory(
incident_date=datetime(2002, 4, 1, tzinfo=pytz.utc),
is_officer_complaint=True
)
allegation_category = AllegationCategoryFactory(category='Use of Force')
OfficerHistoryFactory(
officer=officer, effective_date=datetime(2002, 2, 27, tzinfo=pytz.utc),
unit=PoliceUnitFactory(id=1, unit_name='CAND', description='')
)
ComplainantFactory(allegation=allegation, race='White', age=18, gender='F')
OfficerBadgeNumberFactory(officer=officer, star='123456', current=True)
OfficerBadgeNumberFactory(officer=officer, star='789', current=False)
OfficerAllegationFactory(
officer=officer, allegation=allegation, allegation_category=allegation_category,
final_finding='SU', start_date=date(2002, 3, 2), disciplined=True
)
OfficerAllegationFactory(
officer=officer, allegation=internal_allegation,
final_finding='NS', start_date=date(2002, 3, 2), disciplined=False
)
AwardFactory(officer=officer, award_type='Complimentary Letter', start_date=date(2014, 5, 1))
AwardFactory(officer=officer, award_type='Honored Police Star', start_date=date(2014, 6, 1))
AwardFactory(officer=officer, award_type='Honorable Mention', start_date=date(2014, 7, 1))
SalaryFactory(officer=officer, salary=50000, year=2002)
SalaryFactory(officer=officer, salary=90000, year=2017)
TRRFactory(officer=officer, trr_datetime=datetime(2002, 3, 1, tzinfo=pytz.utc))
second_officer = OfficerFactory(
tags=[],
first_name='Kevin', last_name='Osborn', id=456, race='Black', gender='M',
appointed_date=date(2002, 1, 27), resignation_date=date(2017, 12, 27), rank='PO',
active=ACTIVE_YES_CHOICE, birth_year=1970
)
TRRFactory(officer=second_officer, trr_datetime=datetime(2002, 5, 1, tzinfo=pytz.utc))
TRRFactory(officer=second_officer, trr_datetime=datetime(2002, 12, 1, tzinfo=pytz.utc))
OfficerFactory(
tags=[],
first_name='Kevin', last_name='Edward', id=789, race='Black', gender='M',
appointed_date=date(2002, 3, 27), resignation_date=date(2017, 12, 27), rank='PO',
active=ACTIVE_YES_CHOICE, birth_year=1970
)
officer_cache_manager.build_cached_columns()
allegation_cache_manager.cache_data()
response = self.client.get(reverse('api-v2:officers-mobile-detail', kwargs={'pk': 123}))
expected_response = {
'officer_id': 123,
'unit': {
'unit_id': 1,
'unit_name': 'CAND',
'description': '',
},
'date_of_appt': '2002-02-27',
'date_of_resignation': '2017-12-27',
'active': 'Active',
'rank': 'PO',
'full_name': 'Kevin Kerl',
'race': 'White',
'badge': '123456',
'historic_badges': ['789'],
'gender': 'Male',
'birth_year': 1960,
'sustained_count': 1,
'unsustained_count': 1,
'civilian_compliment_count': 1,
'allegation_count': 2,
'discipline_count': 1,
'honorable_mention_count': 1,
'trr_count': 1,
'major_award_count': 1,
'percentile_allegation': '32.5000',
'percentiles': []
}
expect(response.data).to.eq(expected_response)