def test_extract_datum_with_police_hq(self):
police_district_area = AreaFactory(area_type='police_district', name='22nd')
beat_area = AreaFactory(
name='1',
tags=['tag'],
median_income=343,
area_type='beat',
police_hq=police_district_area)
expect(
AreaIndexer().extract_datum(beat_area)
).to.be.eq({
'name': '1',
'url': beat_area.v1_url,
'area_type': 'beat',
'tags': ['tag', 'beat'],
'allegation_count': 0,
'officers_most_complaint': [],
'most_common_complaint': [],
'race_count': [],
'allegation_percentile': None,
'median_income': 343,
'commander': None,
'alderman': None,
'police_hq': '22nd'
})
def test_build_terms(self):
class MyAreaTermBuilder(AreaTermBuilder):
slug = 'my_area_type'
query_key = 'my_query_key'
AreaFactory(area_type='my_area_type', name='1st')
AreaFactory(area_type='my_area_type', name='2nd')
expect(MyAreaTermBuilder.build_terms()).to.eq([
{
'name': '1st',
'link': 'http://cpdb.lvh.me/url-mediator/session-builder?my_query_key=1st'
}, {
'name': '2nd',
'link': 'http://cpdb.lvh.me/url-mediator/session-builder?my_query_key=2nd'
}
])
def test_extract_datum_police_district_has_no_description(self):
area = AreaFactory(
name='name',
tags=['tag'],
median_income=343,
area_type='police-districts',
alderman='IronMan',
)
expect(
AreaIndexer().extract_datum(area)
).to.be.eq({
'name': 'name',
'url': area.v1_url,
'area_type': 'police-district',
'tags': ['tag', 'police district'],
'allegation_count': 0,
'officers_most_complaint': [],
'most_common_complaint': [],
'race_count': [],
'median_income': 343,
'alderman': 'IronMan',
'commander': None,
'allegation_percentile': None,
'police_hq': None,
})
def test_extract_datum_with_ward_name(self):
area = AreaFactory(
name='name',
tags=['tag'],
median_income=343,
area_type='wards',
alderman='IronMan',
description='Other Name'
)
RacePopulationFactory(
area=area,
race='Asian',
count=101
)
expect(
AreaIndexer().extract_datum(area)
).to.be.eq({
'name': 'name',
'url': area.v1_url,
'area_type': 'ward',
'tags': ['tag', 'ward'],
'allegation_count': 0,
'officers_most_complaint': [],
'most_common_complaint': [],
'race_count': [{
'race': 'Asian',
'count': 101
}],
'median_income': 343,
'alderman': 'IronMan',
'commander': None,
'allegation_percentile': None,
'police_hq': None
})
def test_get_beat(self):
allegation = AllegationFactory(beat=None)
result = CRSerializer(allegation).data
expect(result).to.exclude('beat')
allegation = AllegationFactory(beat=AreaFactory(name='23'))
result = CRSerializer(allegation).data
expect(result['beat']).to.eq('23')
def test_get_community_discipline_count(self):
area = AreaFactory(area_type=COMMUNITY_AREA_CHOICE)
self.make_officer_allegation(5, area, incident_date=datetime(1988, 1, 1, tzinfo=pytz.utc), disciplined=True)
self.make_officer_allegation(5, area, incident_date=datetime(2016, 7, 1, tzinfo=pytz.utc), disciplined=False)
self.make_officer_allegation(2, area, incident_date=datetime(1987, 12, 31, tzinfo=pytz.utc), disciplined=False)
self.make_officer_allegation(2, area, incident_date=datetime.now(pytz.utc), disciplined=True)
result = json.loads(self.command.get_community_data())
expect(result['features']).to.have.length(1)
expect(result['features'][0]['properties']['allegation_count']).to.eq(12)
expect(result['features'][0]['properties']['discipline_count']).to.eq(7)
def test_fix_community_area_type(self):
AreaFactory(area_type='Community', name='UPPER CASE')
self.assertEqual(Area.objects.filter(area_type='Community').count(), 1)
self.assertEqual(Area.objects.filter(area_type='community').count(), 0)
call_command('fix_community_area_type')
self.assertEqual(Area.objects.filter(area_type='Community').count(), 0)
self.assertEqual(Area.objects.filter(area_type='community').count(), 1)
self.assertEqual(Area.objects.first().name, 'Upper Case')
def test_get_queryset_with_police_district(self):
area1 = AreaFactory(area_type='police-districts')
RacePopulationFactory(race='White', count=1000, area=area1)
AllegationFactory.create_batch(2, areas=[area1])
area2 = AreaFactory(area_type='police-districts')
RacePopulationFactory(race='Black', count=100, area=area2)
AllegationFactory(areas=[area2])
area3 = AreaFactory(area_type='community')
RacePopulationFactory(race='Black', count=10, area=area3)
AllegationFactory.create_batch(3, areas=[area3])
area_indexer = AreaIndexer()
expect(area_indexer.get_queryset().count()).to.eq(3)
expect(area_indexer._percentiles).to.eq({
area1.id: 0.0,
area2.id: 50.0
})
def test_get_community_most_complaints_officers(self):
area = AreaFactory(area_type=COMMUNITY_AREA_CHOICE)
officer1 = OfficerFactory(id=12, first_name='John', last_name='Fenedy')
officer2 = OfficerFactory(id=23, first_name='Jerome', last_name='Finnigan')
officer3 = OfficerFactory(id=34, first_name='Raymond', last_name='Piwnicki')
officer4 = OfficerFactory(id=45, first_name='Sean', last_name='Campbell')
min_date = datetime(1988, 1, 1, tzinfo=pytz.utc)
max_date = datetime(2016, 7, 1, tzinfo=pytz.utc)
before_min_date = datetime(1987, 12, 31, tzinfo=pytz.utc)
today = datetime.now(pytz.utc)
self.make_officer_allegation(7, area=area, incident_date=min_date, officer=officer1)
self.make_officer_allegation(6, area=area, incident_date=min_date, officer=officer2)
self.make_officer_allegation(5, area=area, incident_date=min_date, officer=officer3)
self.make_officer_allegation(4, area=area, incident_date=min_date, officer=officer4)
self.make_officer_allegation(7, area=area, incident_date=max_date, officer=officer1)
self.make_officer_allegation(6, area=area, incident_date=max_date, officer=officer2)
self.make_officer_allegation(5, area=area, incident_date=max_date, officer=officer3)
self.make_officer_allegation(4, area=area, incident_date=max_date, officer=officer4)
self.make_officer_allegation(2, area=area, incident_date=before_min_date, officer=officer1)
self.make_officer_allegation(2, area=area, incident_date=before_min_date, officer=officer2)
self.make_officer_allegation(2, area=area, incident_date=before_min_date, officer=officer3)
self.make_officer_allegation(2, area=area, incident_date=before_min_date, officer=officer4)
self.make_officer_allegation(2, area=area, incident_date=today, officer=officer1)
self.make_officer_allegation(2, area=area, incident_date=today, officer=officer2)
self.make_officer_allegation(2, area=area, incident_date=today, officer=officer3)
self.make_officer_allegation(2, area=area, incident_date=today, officer=officer4)
result = json.loads(self.command.get_community_data())
expect(result['features']).to.have.length(1)
expect(result['features'][0]['properties']['most_complaints_officers']).to.eq([
{
'full_name': 'John Fenedy',
'complaints_count': 16,
'id': 12
},
{
'full_name': 'Jerome Finnigan',
'complaints_count': 14,
'id': 23
},
{
'full_name': 'Raymond Piwnicki',
'complaints_count': 12,
'id': 34
}
])
def test_get_community_data(self):
area = AreaFactory(
area_type=COMMUNITY_AREA_CHOICE,
name='Hyde Park',
id=123,
median_income='$60,400',
polygon=MultiPolygon(Polygon((
(87.940101, 42.023135),
(87.523661, 42.023135),
(87.523661, 41.644286),
(87.940101, 41.644286),
(87.940101, 42.023135))))
)
RacePopulationFactory(area=area, race='White', count=1000)
RacePopulationFactory(area=area, race='Black', count=500)
expect(self.command.get_community_data()).to.eq(json.dumps({
'type': 'FeatureCollection',
'features': [{
'type': 'Feature',
'properties': {
'id': 123,
'name': 'Hyde Park',
'allegation_count': 0,
'discipline_count': 0,
'most_complaints_officers': [],
'population': 1500,
'median_income': '$60,400',
'race_count': [{
'race': 'White',
'count': 1000
}, {
'race': 'Black',
'count': 500
}]
},
'geometry': {
'type': 'MultiPolygon',
'coordinates': [[[
[87.940101, 42.023135],
[87.523661, 42.023135],
[87.523661, 41.644286],
[87.940101, 41.644286],
[87.940101, 42.023135]
]]]
}
}]
}))
def test_extract_datum(self):
commander = OfficerFactory(first_name='Captain', last_name='America')
area = AreaFactory(
name='name',
tags=['tag'],
median_income=343,
area_type='police-districts',
commander=commander,
description='Other Name'
)
RacePopulationFactory(
area=area,
race='Asian',
count=101
)
area_indexer = AreaIndexer()
area_indexer._percentiles = {area.id: 0}
expect(
area_indexer.extract_datum(area)
).to.be.eq({
'name': 'Other Name',
'url': area.v1_url,
'area_type': 'police-district',
'tags': ['tag', 'police district'],
'allegation_count': 0,
'officers_most_complaint': [],
'most_common_complaint': [],
'race_count': [{
'race': 'Asian',
'count': 101
}],
'allegation_percentile': 0,
'median_income': 343,
'commander': {
'id': commander.id,
'full_name': 'Captain America',
'allegation_count': 0,
},
'alderman': None,
'police_hq': None
})
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_extract_datum_with_officers_most_complaint(self):
area = AreaFactory(
name='name',
tags=['tag'],
median_income=343,
area_type='police-districts',
alderman='IronMan',
)
area.get_officers_most_complaints = Mock(return_value=[
OfficerFactory.build(
id=123,
first_name='A',
last_name='B',
allegation_count=5,
civilian_allegation_percentile=0,
internal_allegation_percentile=0,
trr_percentile=0,
complaint_percentile=0
),
OfficerFactory.build(
id=456,
first_name='E',
last_name='F',
allegation_count=3,
civilian_allegation_percentile=Decimal(33.3333),
internal_allegation_percentile=0,
trr_percentile=Decimal(33.3333),
complaint_percentile=Decimal(33.3333)
),
OfficerFactory.build(
id=999,
first_name='X',
last_name='Y',
allegation_count=2,
complaint_percentile=None
)
])
area_indexer = AreaIndexer()
expect(area_indexer.extract_datum(area)).to.be.eq({
'name': 'name',
'url': area.v1_url,
'area_type': 'police-district',
'tags': ['tag', 'police district'],
'allegation_count': 0,
'most_common_complaint': [],
'race_count': [],
'median_income': 343,
'alderman': 'IronMan',
'commander': None,
'allegation_percentile': None,
'police_hq': None,
'officers_most_complaint': [
{
'id': 123,
'name': 'A B',
'count': 5,
'percentile_allegation_civilian': '0.0000',
'percentile_allegation_internal': '0.0000',
'percentile_trr': '0.0000',
'percentile_allegation': '0.0000',
}, {
'id': 456,
'name': 'E F',
'count': 3,
'percentile_allegation_civilian': '33.3333',
'percentile_allegation_internal': '0.0000',
'percentile_trr': '33.3333',
'percentile_allegation': '33.3333',
}, {
'id': 999,
'name': 'X Y',
'count': 2
}
],
})
def test_get_queryset(self):
area_indexer = AreaIndexer()
expect(area_indexer.get_queryset().count()).to.eq(0)
AreaFactory()
expect(area_indexer.get_queryset().count()).to.eq(1)
expect(area_indexer._percentiles).to.have.length(0)
def test_export_xlsx(self):
allegation = AllegationFactory(crid='1009678',
location='Tavern/Liquor Store',
add1='37XX',
add2='W 63RD ST',
city='CHICAGO IL',
old_complaint_address=None,
incident_date=datetime(2007,
9,
28,
0,
0,
tzinfo=pytz.utc),
beat=AreaFactory(name='0823'),
is_officer_complaint=True,
coaccused_count=19)
allegation1 = AllegationFactory(
crid='1012803',
location='Public Way - Other',
add1='31XX',
add2='N NEWCASTLE AVE',
city='CHICAGO IL',
old_complaint_address=None,
incident_date=datetime(2005, 11, 1, 0, 0, tzinfo=pytz.utc),
beat=AreaFactory(name='2511'),
is_officer_complaint=False,
coaccused_count=9)
officer = OfficerFactory(
id=8562,
first_name='Jerome',
last_name='Finnigan',
middle_initial='A',
middle_initial2=None,
suffix_name=None,
)
officer1 = OfficerFactory(
first_name='Jeffery',
last_name='Aaron',
middle_initial='M',
middle_initial2=None,
suffix_name=None,
gender='M',
race='White',
appointed_date=date(2005, 9, 26),
resignation_date=None,
rank='Sergeant of Police',
birth_year=1971,
active='Yes',
complaint_percentile=Decimal('61.2357'),
civilian_allegation_percentile=Decimal('61.2069'),
internal_allegation_percentile=Decimal('76.9384'),
trr_percentile=Decimal('79.8763'),
honorable_mention_percentile=Decimal('94.8669'),
allegation_count=6,
sustained_count=0,
honorable_mention_count=61,
unsustained_count=0,
discipline_count=0,
civilian_compliment_count=4,
trr_count=7,
major_award_count=0,
current_badge='1424',
last_unit=PoliceUnitFactory(unit_name='003'),
current_salary=101442,
)
officer2 = OfficerFactory(
first_name='Karina',
last_name='Aaron',
middle_initial=None,
middle_initial2=None,
suffix_name=None,
gender='F',
race='Hispanic',
appointed_date=date(2005, 9, 26),
resignation_date=None,
rank='Police Officer',
birth_year=1980,
active='Yes',
complaint_percentile=Decimal('72.0378'),
civilian_allegation_percentile=Decimal('76.4252'),
internal_allegation_percentile=Decimal('0.0000'),
trr_percentile=Decimal('67.4458'),
honorable_mention_percentile=Decimal('96.0992'),
allegation_count=8,
sustained_count=0,
honorable_mention_count=71,
unsustained_count=2,
discipline_count=0,
civilian_compliment_count=2,
trr_count=4,
major_award_count=0,
current_badge='20373',
last_unit=PoliceUnitFactory(unit_name='001'),
current_salary=94122,
)
allegation_category = AllegationCategoryFactory(
category='Illegal Search',
allegation_name='Improper Search Of Person',
)
allegation_category1 = AllegationCategoryFactory(
category='False Arrest',
allegation_name='Illegal Arrest / False Arrest',
)
OfficerAllegationFactory(
officer=officer,
allegation=allegation,
allegation_category=allegation_category,
start_date=date(2007, 9, 28),
end_date=None,
recc_finding='',
recc_outcome='Unknown',
final_finding='',
final_outcome='Unknown',
disciplined=None,
)
OfficerAllegationFactory(
officer=officer,
allegation=allegation1,
allegation_category=allegation_category1,
start_date=date(2007, 12, 21),
end_date=date(2008, 5, 29),
recc_finding='',
recc_outcome='Unknown',
final_finding='',
final_outcome='Unknown',
disciplined=None,
)
OfficerAllegationFactory(
officer=officer1,
allegation=allegation,
allegation_category=allegation_category,
start_date=date(1967, 10, 21),
end_date=date(1980, 8, 1),
recc_finding='',
recc_outcome='Unknown',
final_finding='SU',
final_outcome='30 Day Suspension',
disciplined=True,
)
OfficerAllegationFactory(
officer=officer2,
allegation=allegation,
allegation_category=allegation_category,
start_date=date(1970, 8, 13),
end_date=date(1973, 9, 15),
recc_finding='',
recc_outcome='Unknown',
final_finding='SU',
final_outcome='Suspended Over 30 Days',
final_outcome_class='',
disciplined=True,
)
VictimFactory(
allegation=allegation,
gender='M',
race='Hispanic',
birth_year=1973,
)
VictimFactory(
allegation=allegation1,
gender='',
race='',
birth_year=None,
)
witness = OfficerFactory(
first_name='Jeffery',
last_name='Aaron',
middle_initial='M',
middle_initial2=None,
suffix_name=None,
gender='M',
race='White',
appointed_date=date(2005, 9, 26),
resignation_date=None,
rank='Sergeant of Police',
birth_year=1971,
active='Yes',
complaint_percentile=Decimal('61.2357'),
civilian_allegation_percentile=Decimal('61.2069'),
internal_allegation_percentile=Decimal('76.9384'),
trr_percentile=Decimal('79.8763'),
honorable_mention_percentile=Decimal('94.8669'),
allegation_count=6,
sustained_count=0,
honorable_mention_count=61,
unsustained_count=0,
discipline_count=0,
civilian_compliment_count=4,
trr_count=7,
major_award_count=0,
current_badge='1424',
last_unit=PoliceUnitFactory(unit_name='003'),
current_salary=101442,
)
PoliceWitnessFactory(
officer=witness,
allegation=allegation,
)
writer = AccusedXlsxWriter(officer, self.test_output_dir)
writer.export_xlsx()
self.covert_xlsx_to_csv('accused.xlsx')
self.assert_csv_files_equal('accused_8562', [
'Allegation', 'Coaccused Officer', 'Beat', 'Police Witness',
'Victim'
])
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'
}
]
})
