def test_victims(self):
allegation = AllegationFactory()
victim1 = VictimFactory(allegation=allegation)
victim2 = VictimFactory(allegation=allegation)
officer_allegation = OfficerAllegationFactory(allegation=allegation)
expect(list(officer_allegation.victims)).to.eq([victim1, victim2])
def test_serialization(self):
victim = VictimFactory(race='Black', gender='M', age=53)
expect(VictimSerializer(victim).data).to.eq({
'race': 'Black',
'gender': 'Male',
'age': 53,
})
def test_serialization(self):
pinned_officer = OfficerFactory(
id=1,
rank='Police Officer',
first_name='Jerome',
last_name='Finnigan',
trr_percentile='11.11',
complaint_percentile='22.22',
civilian_allegation_percentile='33.33',
internal_allegation_percentile='44.44',
allegation_count=2,
)
relevant_allegation = AllegationFactory(
crid='1',
incident_date=datetime(2002, 2, 21, tzinfo=pytz.utc),
most_common_category=AllegationCategoryFactory(
category='Operation/Personnel Violations',
allegation_name='Subcategory'),
point=Point([0.01, 0.02]))
VictimFactory(gender='M',
race='Black',
age=35,
allegation=relevant_allegation)
pinboard = PinboardFactory(
id='66ef1560',
title='Test pinboard',
description='Test description',
)
pinboard.officers.set([pinned_officer])
OfficerAllegationFactory(officer=pinned_officer,
allegation=relevant_allegation)
expect(pinboard.relevant_complaints.count()).to.eq(1)
expect(
AllegationMobileSerializer(
pinboard.relevant_complaints.first()).data).to.eq({
'crid':
'1',
'category':
'Operation/Personnel Violations',
'incident_date':
'2002-02-21',
'officers': [{
'id': 1,
'rank': 'Police Officer',
'full_name': 'Jerome Finnigan',
'percentile_trr': '11.1100',
'percentile_allegation': '22.2200',
'percentile_allegation_civilian': '33.3300',
'percentile_allegation_internal': '44.4400',
}],
'point': {
'lon': 0.01,
'lat': 0.02
},
})
def test_serialization(self):
category = AllegationCategoryFactory(category='Use of Force', allegation_name='Subcategory')
allegation = AllegationFactory(
crid=123,
old_complaint_address='16XX N TALMAN AVE, CHICAGO IL',
most_common_category=category,
incident_date=datetime(2002, 1, 1, tzinfo=pytz.utc),
coaccused_count=12,
point=Point(-35.5, 68.9),
)
VictimFactory(
gender='M',
race='Black',
age=35,
allegation=allegation
)
expect(AllegationSerializer(allegation).data).to.eq({
'crid': '123',
'address': '16XX N TALMAN AVE, CHICAGO IL',
'category': 'Use of Force',
'incident_date': '2002-01-01',
'victims': [
{
'gender': 'Male',
'race': 'Black',
'age': 35
}
],
'point': {
'lon': -35.5,
'lat': 68.9
},
'to': '/complaint/123/',
'sub_category': 'Subcategory',
})
def test_serialization(self):
category = AllegationCategoryFactory(
category='Use of Force',
allegation_name='Improper Search Of Person')
allegation = AllegationFactory(
crid='123',
is_officer_complaint=True,
incident_date=datetime(2005, 12, 31, tzinfo=pytz.utc),
most_common_category=category,
old_complaint_address='34XX Douglas Blvd',
)
attachment = AttachmentFileFactory(tag='TRR',
allegation=allegation,
title='CR document',
id='123456',
url='http://cr-document.com/',
file_type=MEDIA_TYPE_DOCUMENT)
officer = OfficerFactory(id=8562,
first_name='Jerome',
last_name='Finnigan',
allegation_count=5,
trr_percentile=80,
complaint_percentile=85,
civilian_allegation_percentile=90,
internal_allegation_percentile=95)
officer_allegation = OfficerAllegationFactory(
id=1,
officer=officer,
allegation=allegation,
recc_outcome='10 Day Suspension',
final_finding='SU',
final_outcome='Separation',
disciplined=True,
allegation_category=category)
VictimFactory(gender='M', race='Black', age=35, allegation=allegation)
setattr(allegation, 'prefetch_filtered_attachment_files', [attachment])
allegation.officerallegation_set.set([officer_allegation])
expect(SocialGraphCRDetailSerializer(allegation).data).to.eq({
'kind':
'CR',
'crid':
'123',
'to':
'/complaint/123/',
'category':
'Use of Force',
'subcategory':
'Improper Search Of Person',
'incident_date':
'2005-12-31',
'address':
'34XX Douglas Blvd',
'victims': [{
'gender': 'Male',
'race': 'Black',
'age': 35
}],
'coaccused': [{
'id': 8562,
'full_name': 'Jerome Finnigan',
'allegation_count': 5,
'percentile_allegation': '85.0000',
'percentile_allegation_civilian': '90.0000',
'percentile_allegation_internal': '95.0000',
'percentile_trr': '80.0000',
}],
'attachments': [{
'id': '123456',
'title': 'CR document',
'url': 'http://cr-document.com/',
'file_type': MEDIA_TYPE_DOCUMENT,
}],
'officer_ids': [8562],
})
def test_new_timeline_item(self):
officer = OfficerFactory(id=123, appointed_date=date(2000, 1, 1), rank='Police Officer')
unit1 = PoliceUnitFactory(unit_name='001', description='unit_001')
unit2 = PoliceUnitFactory(unit_name='002', description='unit_002')
OfficerHistoryFactory(officer=officer, unit=unit1, effective_date=date(2010, 1, 1), end_date=date(2011, 12, 31))
OfficerHistoryFactory(officer=officer, unit=unit2, effective_date=date(2012, 1, 1), end_date=None)
AwardFactory(officer=officer, start_date=date(2011, 3, 23), award_type='Honorable Mention')
AwardFactory(officer=officer, start_date=date(2015, 3, 23), award_type='Complimentary Letter')
AwardFactory(officer=officer, start_date=date(2011, 3, 23), award_type='Life Saving Award')
allegation = AllegationFactory(
crid='123456',
coaccused_count=4,
incident_date=datetime(2011, 8, 23, tzinfo=pytz.utc)
)
VictimFactory(allegation=allegation, gender='M', race='White', age=34)
OfficerAllegationFactory(
final_finding='UN', final_outcome='Unknown',
officer=officer, allegation=allegation,
allegation_category=AllegationCategoryFactory(category='category', allegation_name='sub category')
)
OfficerAllegationFactory.create_batch(3, allegation=allegation)
allegation2 = AllegationFactory(
crid='654321',
point=Point(35.5, 68.9),
coaccused_count=1,
incident_date=datetime(2015, 8, 23, tzinfo=pytz.utc)
)
OfficerAllegationFactory(
final_finding='UN', final_outcome='9 Day Suspension',
officer=officer, allegation=allegation2,
allegation_category=AllegationCategoryFactory(category='Use of Force', allegation_name='sub category')
)
trr2011 = TRRFactory(
officer=officer,
trr_datetime=datetime(2011, 9, 23, tzinfo=pytz.utc),
taser=True,
firearm_used=False
)
trr2015 = TRRFactory(
officer=officer,
trr_datetime=datetime(2015, 9, 23, tzinfo=pytz.utc),
taser=False,
firearm_used=False
)
SalaryFactory(officer=officer, year=2001, rank='Police Officer', spp_date=date(2001, 9, 23))
SalaryFactory(officer=officer, year=2000, rank='Junior Police Officer', spp_date=date(2000, 1, 1))
cache_managers.cache_all()
response = self.client.get(reverse('api-v2:officers-new-timeline-items', kwargs={'pk': 123}))
expect(response.status_code).to.eq(status.HTTP_200_OK)
expect(response.data).to.eq([
{
'trr_id': trr2015.id,
'date': '2015-09-23',
'kind': 'FORCE',
'taser': False,
'firearm_used': False,
'unit_name': '002',
'unit_description': 'unit_002',
'rank': 'Police Officer',
}, {
'date': '2015-08-23',
'kind': 'CR',
'crid': '654321',
'category': 'Use of Force',
'subcategory': 'sub category',
'finding': 'Unfounded',
'outcome': '9 Day Suspension',
'coaccused': 1,
'unit_name': '002',
'unit_description': 'unit_002',
'rank': 'Police Officer',
'point': {
'lon': 35.5,
'lat': 68.9
},
'victims': [],
'attachments': []
}, {
'date': '2012-01-01',
'kind': 'UNIT_CHANGE',
'unit_name': '002',
'unit_description': 'unit_002',
'rank': 'Police Officer',
}, {
'trr_id': trr2011.id,
'date': '2011-09-23',
'kind': 'FORCE',
'taser': True,
'firearm_used': False,
'unit_name': '001',
'unit_description': 'unit_001',
'rank': 'Police Officer',
}, {
'date': '2011-08-23',
'kind': 'CR',
'crid': '123456',
'category': 'category',
'subcategory': 'sub category',
'finding': 'Unfounded',
'outcome': 'Unknown',
'coaccused': 4,
'unit_name': '001',
'unit_description': 'unit_001',
'rank': 'Police Officer',
'victims': [
{
'race': 'White',
'age': 34,
'gender': 'Male',
}
],
'attachments': [],
}, {
'date': '2011-03-23',
'kind': 'AWARD',
'award_type': 'Life Saving Award',
'unit_name': '001',
'unit_description': 'unit_001',
'rank': 'Police Officer',
}, {
'date': '2010-01-01',
'kind': 'UNIT_CHANGE',
'unit_name': '001',
'unit_description': 'unit_001',
'rank': 'Police Officer',
}, {
'date': '2001-09-23',
'kind': 'RANK_CHANGE',
'unit_name': '',
'unit_description': '',
'rank': 'Police Officer',
}, {
'date': '2000-01-01',
'kind': 'JOINED',
'unit_name': '',
'unit_description': '',
'rank': 'Junior Police Officer',
},
])
def test_serialization(self):
pinned_officer = OfficerFactory(
id=1,
rank='Police Officer',
first_name='Jerome',
last_name='Finnigan',
allegation_count=10,
trr_percentile='99.99',
complaint_percentile='88.88',
civilian_allegation_percentile='77.77',
internal_allegation_percentile='66.66',
)
relevant_allegation = AllegationFactory(
crid='1',
add1='LTK street',
incident_date=datetime(2002, 2, 21, tzinfo=pytz.utc),
most_common_category=AllegationCategoryFactory(
category='Operation/Personnel Violations', allegation_name='Miscellaneous'
),
point=Point([0.01, 0.02]),
)
VictimFactory(allegation=relevant_allegation, gender='F', age=65)
VictimFactory(allegation=relevant_allegation, gender='M', age=54)
AttachmentFileFactory(
id=1,
file_type='document',
title='relevant document 1',
allegation=relevant_allegation,
show=True,
preview_image_url="https://assets.documentcloud.org/CRID-1-CR-p1-normal.gif",
url='http://cr-1-document.com/',
)
pinboard = PinboardFactory(
id='66ef1560',
title='Test pinboard',
description='Test description',
)
pinboard.officers.set([pinned_officer])
OfficerAllegationFactory(officer=pinned_officer, allegation=relevant_allegation)
expect(pinboard.relevant_documents.count()).to.eq(1)
expect(AllegationSerializer(pinboard.relevant_documents[0].allegation).data).to.eq({
'crid': '1',
'address': 'LTK street',
'category': 'Operation/Personnel Violations',
'incident_date': '2002-02-21',
'coaccused': [{
'id': 1,
'rank': 'Police Officer',
'full_name': 'Jerome Finnigan',
'percentile_allegation': '88.8800',
'percentile_allegation_civilian': '77.7700',
'percentile_allegation_internal': '66.6600',
'percentile_trr': '99.9900',
'allegation_count': 10,
}],
'point': {
'lon': 0.01,
'lat': 0.02,
},
'victims': [
{
'gender': 'Female',
'race': 'Black',
'age': 65
},
{
'gender': 'Male',
'race': 'Black',
'age': 54
}
],
'sub_category': 'Miscellaneous',
'to': '/complaint/1/',
})
expect(DocumentSerializer(pinboard.relevant_documents.first()).data).to.eq({
'id': 1,
'preview_image_url': "https://assets.documentcloud.org/CRID-1-CR-p1-normal.gif",
'url': 'http://cr-1-document.com/',
'allegation': {
'address': 'LTK street',
'crid': '1',
'category': 'Operation/Personnel Violations',
'incident_date': '2002-02-21',
'coaccused': [{
'id': 1,
'rank': 'Police Officer',
'full_name': 'Jerome Finnigan',
'allegation_count': 10,
'percentile_allegation': '88.8800',
'percentile_allegation_civilian': '77.7700',
'percentile_allegation_internal': '66.6600',
'percentile_trr': '99.9900',
}],
'point': {
'lon': 0.01,
'lat': 0.02,
},
'victims': [
{
'gender': 'Female',
'race': 'Black',
'age': 65
},
{
'gender': 'Male',
'race': 'Black',
'age': 54
}
],
'sub_category': 'Miscellaneous',
'to': '/complaint/1/',
},
})
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):
category = AllegationCategoryFactory(category='Use of Force',
allegation_name='Subcategory')
category2 = AllegationCategoryFactory(category='Verbal Abuse',
allegation_name='Miscellaneous')
allegation = AllegationFactory(
crid=123,
old_complaint_address='16XX N TALMAN AVE, CHICAGO IL',
most_common_category=category,
incident_date=datetime(2002, 1, 1, tzinfo=pytz.utc),
coaccused_count=12,
point=Point(-35.5, 68.9),
)
VictimFactory(gender='M', race='Black', age=35, allegation=allegation)
coaccused = OfficerFactory(id=2,
first_name='Walter',
last_name='White',
allegation_count=6,
sustained_count=5,
birth_year=1940,
race='White',
gender='M',
rank='Sergeant of Police',
complaint_percentile=0.0,
civilian_allegation_percentile=1.1,
internal_allegation_percentile=2.2,
trr_percentile=3.3,
resignation_date=date(2015, 4, 14))
OfficerAllegationFactory(
officer=coaccused,
allegation=allegation,
recc_outcome='11 Day Suspension',
final_outcome='Separation',
final_finding='SU',
allegation_category=category2,
disciplined=True,
)
expect(PinnedAllegationSerializer(allegation).data).to.eq({
'crid':
'123',
'address':
'16XX N TALMAN AVE, CHICAGO IL',
'category':
'Use of Force',
'incident_date':
'2002-01-01',
'victims': [{
'gender': 'Male',
'race': 'Black',
'age': 35
}],
'point': {
'lon': -35.5,
'lat': 68.9
},
'to':
'/complaint/123/',
'sub_category':
'Subcategory',
'coaccused': [{
'id': 2,
'full_name': 'Walter White',
'complaint_count': 6,
'sustained_count': 5,
'birth_year': 1940,
'recommended_outcome': '11 Day Suspension',
'final_outcome': 'Separation',
'final_finding': 'Sustained',
'category': 'Verbal Abuse',
'disciplined': True,
'race': 'White',
'gender': 'Male',
'rank': 'Sergeant of Police',
'percentile_trr': '3.3000',
'percentile_allegation': '0.0000',
'percentile_allegation_civilian': '1.1000',
'percentile_allegation_internal': '2.2000'
}],
})
def test_serialization(self):
officer = OfficerFactory(id=123)
allegation = AllegationFactory(
crid='CR123',
incident_date=datetime(2002, 2, 3, tzinfo=pytz.utc),
coaccused_count=3,
point=Point([0.01, 0.02]),
)
allegation_category = AllegationCategoryFactory(
category='some category', allegation_name='some sub category')
officer_allegation = OfficerAllegationFactory(
officer=officer,
allegation=allegation,
start_date=date(2002, 2, 3),
allegation_category=allegation_category,
final_finding='SU',
final_outcome='9 Day Suspension')
attachment = AttachmentFileFactory(
tag='Other',
allegation=allegation,
title='title',
id='123456',
url='url',
preview_image_url='preview_image_url',
file_type='document')
VictimFactory(allegation=allegation, gender='M', race='Black', age=30)
setattr(officer_allegation, 'unit_name', 'Unit 001')
setattr(officer_allegation, 'unit_description', 'District 001')
setattr(officer_allegation, 'rank_name', 'Police Officer')
setattr(allegation, 'prefetch_filtered_attachments', [attachment])
expect(CRNewTimelineSerializer(officer_allegation).data).to.eq({
'unit_name':
'Unit 001',
'unit_description':
'District 001',
'rank':
'Police Officer',
'priority_sort':
30,
'kind':
'CR',
'date_sort':
date(2002, 2, 3),
'date':
'2002-02-03',
'crid':
'CR123',
'category':
'some category',
'subcategory':
'some sub category',
'finding':
'Sustained',
'outcome':
'9 Day Suspension',
'coaccused':
3,
'attachments': [{
'title': 'title',
'url': 'url',
'preview_image_url': 'preview_image_url',
'file_type': 'document',
'id': '123456',
}],
'point': {
'lon': 0.01,
'lat': 0.02
},
'victims': [{
'gender': 'Male',
'race': 'Black',
'age': 30
}]
})
def test_extract_datum(self):
allegation = AllegationFactory(
crid='123456',
incident_date=datetime(2017, 7, 27, tzinfo=pytz.utc),
summary='abc',
add1='3000',
add2='Michigan Ave',
city='Chicago IL'
)
officer = OfficerFactory(
id=10,
first_name='Luke',
last_name='Skywalker',
allegation_count=4,
complaint_percentile='99.70',
trr_percentile='99.88',
civilian_allegation_percentile='77.66',
internal_allegation_percentile='66.55'
)
officer2 = OfficerFactory(
id=11,
first_name='John', last_name='Doe',
allegation_count=2,
complaint_percentile='70.33',
trr_percentile='66.88',
civilian_allegation_percentile='33.66',
internal_allegation_percentile='22.55'
)
OfficerAllegationFactory(allegation=allegation, officer=officer)
category1 = AllegationCategoryFactory(
category='Operation/Personnel Violations',
allegation_name='Secondary/Special Employment'
)
category2 = AllegationCategoryFactory(category='Use of Force', allegation_name='sub category')
OfficerAllegationFactory(allegation=allegation, allegation_category=category2, officer=officer2)
OfficerAllegationFactory.create_batch(2, allegation=allegation, allegation_category=category1, officer=None)
OfficerAllegationFactory.create_batch(3, allegation=allegation, allegation_category=None, officer=None)
VictimFactory(allegation=allegation, gender='F', race='Black', age=25)
VictimFactory(allegation=allegation, gender='', race='Black', age=25)
VictimFactory(allegation=allegation, gender='F', race='Black', age=None)
AttachmentFileFactory(id=1, allegation=allegation, text_content='')
AttachmentFileFactory(
id=2, allegation=allegation, show=False,
text_content="CHICAGO POLICE DEPARTMENT RD I HT334604"
)
AttachmentFileFactory(id=3, allegation=allegation, text_content='CHICAGO POLICE DEPARTMENT RD I HT334604')
setattr(allegation, 'investigator_names', ['Jerome Finnigan'])
allegation_cache_manager.cache_data()
allegation.refresh_from_db()
datum = CrIndexer().extract_datum(allegation)
datum['victims'] = sorted(
datum['victims'],
key=lambda victim: (victim['gender'], victim['race'], victim.get('age', 0))
)
expect(datum).to.eq({
'crid': '123456',
'category': 'Operation/Personnel Violations',
'sub_category': 'Secondary/Special Employment',
'incident_date': '2017-07-27',
'address': '3000 Michigan Ave, Chicago IL',
'summary': 'abc',
'to': '/complaint/123456/',
'investigator_names': ['Jerome Finnigan'],
'victims': [
{'gender': '', 'race': 'Black', 'age': 25},
{'gender': 'Female', 'race': 'Black'},
{'gender': 'Female', 'race': 'Black', 'age': 25},
],
'coaccused': [
{
'id': 10, 'full_name': 'Luke Skywalker', 'allegation_count': 4,
'percentile_allegation': '99.7000',
'percentile_allegation_civilian': '77.6600',
'percentile_allegation_internal': '66.5500',
'percentile_trr': '99.8800',
},
{
'id': 11, 'full_name': 'John Doe', 'allegation_count': 2,
'percentile_allegation': '70.3300',
'percentile_allegation_civilian': '33.6600',
'percentile_allegation_internal': '22.5500',
'percentile_trr': '66.8800',
}
],
'attachment_files': [
{'id': 3, 'text_content': 'CHICAGO POLICE DEPARTMENT RD I HT334604'}
]
})
def test_serialization(self):
category = AllegationCategoryFactory(category='Use of Force', allegation_name='Subcategory')
allegation = AllegationFactory(
crid=123,
incident_date=datetime(2002, 1, 1, tzinfo=pytz.utc),
most_common_category=category,
coaccused_count=12,
point=Point(-35.5, 68.9),
old_complaint_address='34XX Douglas Blvd'
)
officer = OfficerFactory(
id=1,
first_name='Jerome',
last_name='Finnigan',
allegation_count=20,
trr_percentile=80,
complaint_percentile=85,
civilian_allegation_percentile=90,
internal_allegation_percentile=95
)
OfficerAllegationFactory(
officer=officer,
allegation=allegation,
recc_outcome='Separation',
final_outcome='30 Day Suspension',
final_finding='UN',
allegation_category=category,
disciplined=True
)
VictimFactory(
gender='M',
race='Black',
age=35,
allegation=allegation
)
expected_data = {
'incident_date': '2002-01-01',
'crid': '123',
'category': 'Use of Force',
'subcategory': 'Subcategory',
'kind': 'CR',
'address': '34XX Douglas Blvd',
'to': '/complaint/123/',
'victims': [
{
'gender': 'Male',
'race': 'Black',
'age': 35,
}
],
'coaccused': [
{
'id': 1,
'full_name': 'Jerome Finnigan',
'allegation_count': 20,
'percentile_trr': '80.0000',
'percentile_allegation': '85.0000',
'percentile_allegation_civilian': '90.0000',
'percentile_allegation_internal': '95.0000',
}
]
}
expect(CRDetailSerializer(allegation).data).to.eq(expected_data)
def test_gender_display(self):
expect(VictimFactory(gender='M').gender_display).to.equal('Male')
expect(VictimFactory(gender='F').gender_display).to.equal('Female')
expect(VictimFactory(gender='X').gender_display).to.equal('X')
expect(VictimFactory(gender='?').gender_display).to.equal('?')
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_extract_datum_victim_blank_gender(self):
VictimFactory(gender='')
rows = self.extract_data()
expect(rows).to.have.length(1)
expect(rows[0]['victims'][0]['gender']).to.be.none()
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'
}
]
})