def test_complainant_gender_aggregation(self):
unit = PoliceUnitFactory()
officer = OfficerFactory()
allegation1 = AllegationFactory()
allegation2 = AllegationFactory()
OfficerHistoryFactory(officer=officer, unit=unit)
OfficerAllegationFactory(officer=officer,
allegation=allegation1,
final_finding='SU')
OfficerAllegationFactory(officer=officer,
allegation=allegation2,
final_finding='UN')
ComplainantFactory(allegation=allegation1, gender='F')
ComplainantFactory(allegation=allegation2, gender='')
expect(
sorted(unit.complainant_gender_aggregation,
key=itemgetter('name'))).to.eq([{
'name': 'Female',
'count': 1,
'sustained_count': 1
}, {
'name': 'Unknown',
'count': 1,
'sustained_count': 0
}])
def test_complainant_genders(self):
allegation = AllegationFactory()
ComplainantFactory(gender='F', allegation=allegation)
expect(allegation.complainant_genders).to.eq(['Female'])
ComplainantFactory(gender='U', allegation=allegation)
expect(allegation.complainant_genders).to.eq(['Female', 'Unknown'])
def test_complainant_races(self):
allegation = AllegationFactory()
ComplainantFactory(race='White', allegation=allegation)
expect(allegation.complainant_races).to.eq(['White'])
ComplainantFactory(race='White/Hispinic', allegation=allegation)
expect(allegation.complainant_races).to.eq(['White', 'White/Hispinic'])
def test_complainant_age_groups(self):
allegation = AllegationFactory()
ComplainantFactory(age=32, allegation=allegation)
expect(allegation.complainant_age_groups).to.eq(['31-40'])
ComplainantFactory(age=38, allegation=allegation)
expect(allegation.complainant_age_groups).to.eq(['31-40'])
ComplainantFactory(age=55, allegation=allegation)
expect(allegation.complainant_age_groups).to.eq(['31-40', '51+'])
allegation_no_complainant = AllegationFactory()
expect(allegation_no_complainant.complainant_age_groups).to.eq(['Unknown'])
def test_complainant_race_aggregation(self):
officer = OfficerFactory()
allegation1 = AllegationFactory()
allegation2 = AllegationFactory()
allegation3 = AllegationFactory()
OfficerAllegationFactory(officer=officer,
allegation=allegation1,
start_date=date(2010, 1, 1),
final_finding='SU')
OfficerAllegationFactory(officer=officer,
allegation=allegation2,
start_date=date(2011, 1, 1),
final_finding='NS')
OfficerAllegationFactory(officer=officer,
allegation=allegation3,
start_date=None,
final_finding='NS')
ComplainantFactory(allegation=allegation1, race='White')
ComplainantFactory(allegation=allegation2, race='')
ComplainantFactory(allegation=allegation3, race='White')
expect(officer.complainant_race_aggregation).to.eq([{
'name':
'White',
'count':
2,
'sustained_count':
1,
'items': [{
'year': 2010,
'count': 1,
'sustained_count': 1,
'name': 'White'
}]
}, {
'name':
'Unknown',
'count':
1,
'sustained_count':
0,
'items': [{
'year': 2011,
'count': 1,
'sustained_count': 0,
'name': 'Unknown'
}]
}])
def test_extract_info_complaint_record_complainant_null_age(self):
allegation = AllegationFactory()
ComplainantFactory(allegation=allegation, age=None)
OfficerAllegationFactory(final_finding='UN',
start_date=date(2000, 1, 2),
allegation=allegation)
rows = self.extract_data()
expect(rows).to.have.length(1)
expect(rows[0]['complaint_records']['facets'][2]).to.eq({
'name':
'complainant age',
'entries': [{
'name':
'Unknown',
'count':
1,
'sustained_count':
0,
'items': [{
'year': 2000,
'name': 'Unknown',
'count': 1,
'sustained_count': 0
}]
}]
})
def test_complainant_age_aggregation(self):
unit = PoliceUnitFactory()
officer = OfficerFactory()
allegation1 = AllegationFactory()
allegation2 = AllegationFactory()
OfficerHistoryFactory(officer=officer, unit=unit)
OfficerAllegationFactory(officer=officer, allegation=allegation1, final_finding='SU')
OfficerAllegationFactory(officer=officer, allegation=allegation2, final_finding='UN')
ComplainantFactory(allegation=allegation1, age=25)
ComplainantFactory(allegation=allegation2, age=None)
expect(unit.complainant_age_aggregation).to.eq([{
'name': '21-30',
'count': 1,
'sustained_count': 1
}, {
'name': 'Unknown',
'count': 1,
'sustained_count': 0
}])
def test_complainant_race_aggregation(self):
unit = PoliceUnitFactory()
officer = OfficerFactory()
allegation = AllegationFactory()
OfficerHistoryFactory(unit=unit, officer=officer)
OfficerAllegationFactory(officer=officer, allegation=allegation, final_finding='NS')
ComplainantFactory(allegation=allegation, race='White')
expect(unit.complainant_race_aggregation).to.eq([{
'name': 'White',
'count': 1,
'sustained_count': 0
}])
def test_complainant_gender_aggregation_with_duplicated_allegation(self):
unit = PoliceUnitFactory()
officer1 = OfficerFactory()
officer2 = OfficerFactory()
allegation = AllegationFactory()
OfficerHistoryFactory(officer=officer1, unit=unit)
OfficerHistoryFactory(officer=officer2, unit=unit)
OfficerAllegationFactory(officer=officer1, allegation=allegation, final_finding='SU')
OfficerAllegationFactory(officer=officer2, allegation=allegation, final_finding='SU')
ComplainantFactory(allegation=allegation, gender='F')
expect(unit.complainant_gender_aggregation).to.eq([{
'name': 'Female',
'count': 1,
'sustained_count': 1
}])
def test_return_correct_response(self):
officer = OfficerFactory(first_name='John', last_name='Hurley')
OfficerAllegationFactory(allegation=self.allegation, officer=officer)
complaint = OfficerAllegationFactory(
allegation__point=Point([0.01, 0.01]),
allegation__incident_date=datetime(2016, 2, 23, tzinfo=pytz.utc),
officer=officer,
allegation_category__category='False Arrest')
ComplainantFactory(allegation=complaint.allegation,
gender='M',
race='Black',
age='18')
self.refresh_index()
response = self.search(self.allegation.crid, {
'match': 'officers',
'distance': '10mi'
})
expect(response.status_code).to.eq(status.HTTP_200_OK)
expect(response.data).to.eq({
'count':
1,
'previous':
None,
'next':
None,
'results': [{
'crid':
str(complaint.allegation.crid),
'coaccused': ['J. Hurley'],
'category_names': [
'False Arrest',
],
'complainants': [{
'race': 'Black',
'gender': 'Male',
'age': 18
}],
'point': {
'lat': 0.01,
'lon': 0.01
},
'incident_date':
'2016-02-23',
}]
})
def test_query_matching_allegation_with_same_officers(self):
officer = OfficerFactory()
OfficerAllegationFactory(allegation=self.allegation, officer=officer)
complaint = OfficerAllegationFactory(allegation__point=Point(
[0.01, 0.01]),
officer=officer)
ComplainantFactory(allegation=complaint.allegation,
gender='M',
race='Black',
age='18')
OfficerAllegationFactory(allegation__point=Point([0.01, 0.01]))
self.refresh_index()
response = self.search(self.allegation.crid, {
'match': 'officers',
'distance': '1mi'
})
expect(response.status_code).to.eq(status.HTTP_200_OK)
expect(response.data['count']).to.eq(1)
expect(response.data['results'][0]['crid']).to.eq(
str(complaint.allegation.crid))
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)
def test_extract_info_complaint_record_null_complainant(self):
officer = OfficerFactory()
OfficerAllegationFactory(officer=officer,
final_finding='UN',
start_date=date(2000, 1, 2))
allegation = AllegationFactory()
ComplainantFactory(allegation=allegation,
race='White',
gender='M',
age=21)
OfficerAllegationFactory(officer=officer,
allegation=allegation,
final_finding='UN',
start_date=None)
rows = self.extract_data()
expect(rows).to.have.length(1)
expect(rows[0]['complaint_records']['facets'][1:]).to.eq([{
'name':
'complainant race',
'entries': [{
'name':
'Unknown',
'count':
1,
'sustained_count':
0,
'items': [{
'year': 2000,
'name': 'Unknown',
'count': 1,
'sustained_count': 0
}]
}, {
'name':
'White',
'count':
1,
'sustained_count':
0,
'items': [{
'year': None,
'name': 'White',
'count': 1,
'sustained_count': 0
}]
}]
}, {
'name':
'complainant age',
'entries': [{
'name':
'Unknown',
'count':
1,
'sustained_count':
0,
'items': [{
'year': 2000,
'name': 'Unknown',
'count': 1,
'sustained_count': 0
}]
}, {
'name':
'20-30',
'count':
1,
'sustained_count':
0,
'items': [{
'year': None,
'name': '20-30',
'count': 1,
'sustained_count': 0
}]
}]
}, {
'name':
'complainant gender',
'entries': [{
'name':
'Unknown',
'count':
1,
'sustained_count':
0,
'items': [{
'year': 2000,
'name': 'Unknown',
'count': 1,
'sustained_count': 0
}]
}, {
'name':
'Male',
'count':
1,
'sustained_count':
0,
'items': [{
'year': None,
'name': 'Male',
'count': 1,
'sustained_count': 0
}]
}]
}])
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_gender_display(self):
expect(ComplainantFactory(gender='M').gender_display).to.equal('Male')
expect(
ComplainantFactory(gender='F').gender_display).to.equal('Female')
expect(ComplainantFactory(gender='X').gender_display).to.equal('X')
expect(ComplainantFactory(gender='?').gender_display).to.equal('?')
def test_emit_correct_format(self):
allegation = AllegationFactory(
crid='12345',
summary='Summary',
point=Point(12, 21),
incident_date=datetime(2002, 2, 28, tzinfo=pytz.utc),
add1='3510',
add2='Michigan Ave',
city='Chicago',
location='Police Building',
beat=AreaFactory(name='23'),
is_officer_complaint=False,
first_start_date=date(2003, 3, 28),
first_end_date=date(2003, 4, 28),
)
coaccused = OfficerFactory(
id=1,
first_name='Foo',
last_name='Bar',
gender='M',
race='White',
birth_year=1986,
appointed_date=date(2001, 1, 1),
rank='Officer',
complaint_percentile=Decimal(0),
civilian_allegation_percentile=Decimal(1.1),
internal_allegation_percentile=Decimal(2.2),
trr_percentile=Decimal(3.3),
allegation_count=1,
sustained_count=1,
)
OfficerAllegationFactory(
officer=coaccused,
allegation=allegation,
final_finding='SU',
recc_outcome='Separation',
final_outcome='Reprimand',
start_date=date(2003, 3, 28),
end_date=date(2003, 4, 28),
allegation_category=AllegationCategoryFactory(
category='Operation/Personnel Violations',
allegation_name='NEGLECT OF DUTY/CONDUCT UNBECOMING - ON DUTY'
),
disciplined=True
)
ComplainantFactory(allegation=allegation, gender='M', race='White', age=30)
ComplainantFactory(allegation=allegation, gender='F', race='Black', age=25)
VictimFactory(allegation=allegation, gender='F', race='Black', age=25)
VictimFactory(allegation=allegation, gender='M', race='Hispanic', age=40)
officer = OfficerFactory(
id=2,
first_name='Jerome',
last_name='Finnigan',
gender='M',
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=3,
first_name='German',
last_name='Lauren',
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='Other',
allegation=allegation,
file_type='document',
title='CR document',
url='http://foo.com/',
preview_image_url='http://web.com/image',
)
AttachmentFileFactory(
tag='Other',
allegation=allegation,
file_type='document',
title='CR document',
url='http://foo.com/',
preview_image_url='http://web.com/image',
show=False
)
AttachmentFileFactory(
allegation=allegation,
file_type='document',
tag='OCIR',
title='CR document',
url='http://foo.com/',
preview_image_url='http://web.com/image',
)
indexer = CRIndexer()
rows = list(indexer.get_queryset())
row = [obj for obj in rows if obj['crid'] == '12345'][0]
result = indexer.extract_datum(row)
expect(dict(result)).to.eq({
'crid': '12345',
'most_common_category': {
'category': 'Operation/Personnel Violations',
'allegation_name': 'NEGLECT OF DUTY/CONDUCT UNBECOMING - ON DUTY'
},
'category_names': ['Operation/Personnel Violations'],
'coaccused': [
{
'id': 1,
'full_name': 'Foo Bar',
'abbr_name': 'F. Bar',
'gender': 'Male',
'race': 'White',
'rank': 'Officer',
'final_finding': 'Sustained',
'recc_outcome': 'Separation',
'final_outcome': 'Reprimand',
'category': 'Operation/Personnel Violations',
'subcategory': 'NEGLECT OF DUTY/CONDUCT UNBECOMING - ON DUTY',
'start_date': '2003-03-28',
'end_date': '2003-04-28',
'age': 32,
'allegation_count': 1,
'sustained_count': 1,
'percentile_allegation': Decimal('0'),
'percentile_allegation_civilian': Decimal('1.1'),
'percentile_allegation_internal': Decimal('2.2'),
'percentile_trr': Decimal('3.3'),
'disciplined': True
}
],
'complainants': [
{'gender': 'Male', 'race': 'White', 'age': 30},
{'gender': 'Female', 'race': 'Black', 'age': 25}
],
'victims': [
{'gender': 'Female', 'race': 'Black', 'age': 25},
{'gender': 'Male', 'race': 'Hispanic', 'age': 40}
],
'summary': 'Summary',
'point': {'lon': 12.0, 'lat': 21.0},
'incident_date': '2002-02-28',
'start_date': '2003-03-28',
'end_date': '2003-04-28',
'address': '3510 Michigan Ave, Chicago',
'location': 'Police Building',
'beat': '23',
'involvements': [
{
'involved_type': 'investigator',
'officer_id': 3,
'full_name': 'German Lauren',
'abbr_name': 'G. Lauren',
'num_cases': 1,
'current_rank': 'IPRA investigator',
'percentile_allegation': Decimal('6.6000'),
'percentile_allegation_civilian': Decimal('7.7000'),
'percentile_allegation_internal': Decimal('8.8000'),
'percentile_trr': None
},
{
'involved_type': 'police_witness',
'officer_id': 2,
'full_name': 'Jerome Finnigan',
'abbr_name': 'J. Finnigan',
'gender': 'Male',
'race': 'White',
'allegation_count': 1,
'sustained_count': 1,
'percentile_allegation': Decimal('4.4000'),
'percentile_allegation_civilian': None,
'percentile_allegation_internal': None,
'percentile_trr': Decimal('5.5000')
}
],
'attachments': [
{
'title': 'CR document',
'file_type': 'document',
'url': 'http://foo.com/',
'preview_image_url': 'http://web.com/image'
}
]
})
def test_query(self):
trr_officer_1 = OfficerFactory()
trr_officer_2 = OfficerFactory()
pinboard_officer = OfficerFactory()
other_officer = OfficerFactory()
pinboard_allegation = AllegationFactory()
allegation1 = AllegationFactory()
allegation2 = AllegationFactory()
allegation3 = AllegationFactory()
allegation4 = AllegationFactory()
other_allegation = AllegationFactory()
OfficerAllegationFactory(allegation=pinboard_allegation,
officer=other_officer)
OfficerAllegationFactory(allegation=allegation1, officer=trr_officer_1)
OfficerAllegationFactory(allegation=allegation2, officer=trr_officer_2)
OfficerAllegationFactory(allegation=allegation3, officer=trr_officer_2)
OfficerAllegationFactory(allegation=allegation4,
officer=pinboard_officer)
ComplainantFactory(allegation=pinboard_allegation,
gender='M',
race='White')
ComplainantFactory(allegation=pinboard_allegation,
gender='F',
race='White')
ComplainantFactory(allegation=pinboard_allegation,
gender='F',
race='White')
ComplainantFactory(allegation=pinboard_allegation,
gender='F',
race='Black')
ComplainantFactory(allegation=allegation1, gender='X', race='Black')
ComplainantFactory(allegation=allegation1, gender='F', race='Black')
ComplainantFactory(allegation=allegation1, gender='X', race='')
ComplainantFactory(allegation=allegation1,
gender='',
race='Asian/Pacific')
ComplainantFactory(allegation=allegation3, gender='F', race='Hispanic')
ComplainantFactory(allegation=allegation4, gender='M', race='White')
ComplainantFactory(allegation=other_allegation, gender='M', race='')
ComplainantFactory(allegation=other_allegation,
gender='F',
race='Black')
pinboard_trr1 = TRRFactory(officer=trr_officer_1)
pinboard_trr2 = TRRFactory(officer=trr_officer_2)
pinboard = PinboardFactory(trrs=(pinboard_trr1, pinboard_trr2),
allegations=(pinboard_allegation, ),
officers=(pinboard_officer, ))
query_results = dict(ComplainantsSummaryQuery(pinboard).query())
expect(list(query_results['race'])).to.eq([{
'race': 'Black',
'percentage': 0.3
}, {
'race': 'White',
'percentage': 0.4
}, {
'race': 'Hispanic',
'percentage': 0.1
}, {
'race': 'Other',
'percentage': 0.2
}])
expect(list(query_results['gender'])).to.eq([{
'gender': 'M',
'percentage': 0.2
}, {
'gender': 'F',
'percentage': 0.5
}, {
'gender': 'Unknown',
'percentage': 0.3
}])
def test_complainants(self):
allegation = AllegationFactory()
ComplainantFactory(id=1, allegation=allegation)
expect(allegation.complainants.count()).to.eq(1)
expect(allegation.complainants[0].id).to.eq(1)
def test_summary(self):
unit = PoliceUnitFactory(unit_name='123', description='foo')
officer = OfficerFactory(race='White', gender='F', birth_year='1980')
OfficerHistoryFactory(unit=unit, officer=officer, end_date=None)
allegation = AllegationFactory()
allegation_category = AllegationCategoryFactory(
category='Use of Force')
OfficerAllegationFactory(officer=officer,
allegation=allegation,
allegation_category=allegation_category,
final_finding='SU')
ComplainantFactory(allegation=allegation,
race='Black',
age=25,
gender='M')
self.refresh_index()
response = self.client.get(
reverse('api-v2:units-summary', kwargs={'pk': '123'}))
expect(response.data).to.be.eq({
'unit_name': '123',
'description': 'foo',
'member_records': {
'active_members':
1,
'total':
1,
'facets': [{
'name': 'race',
'entries': [{
'name': 'White',
'count': 1
}]
}, {
'name': 'age',
'entries': [{
'name': '31-40',
'count': 1
}]
}, {
'name': 'gender',
'entries': [{
'name': 'Female',
'count': 1
}]
}]
},
'complaint_records': {
'count':
1,
'sustained_count':
1,
'facets': [{
'name':
'category',
'entries': [{
'name': 'Use of Force',
'count': 1,
'sustained_count': 1
}]
}, {
'name':
'race',
'entries': [{
'name': 'Black',
'count': 1,
'sustained_count': 1
}]
}, {
'name':
'age',
'entries': [{
'name': '21-30',
'count': 1,
'sustained_count': 1
}]
}, {
'name':
'gender',
'entries': [{
'name': 'Male',
'count': 1,
'sustained_count': 1
}]
}]
}
})
